Three companies shipped reasoning-tier models within an eight-week window this summer, and none of them agree on what “reasoning” should cost. OpenAI’s GPT-5.6 Sol landed July 9, 2026. Anthropic followed with Claude Fable 5.1 on September 1. Google rounded out the trio with Gemini 3.8 Flash on September 3. Each vendor now exposes a tunable thinking budget, a million-token-class context window, and a very different price tag for the privilege of using it.
The gap between them isn’t cosmetic. On Anthropic’s own Terminal-Bench-Science 0.1 evaluation, Claude Fable 5.1 scored 52.6% against GPT-5.6 Sol’s 22.4%, a 30.2-point split on a benchmark built to test agentic scientific research. Flip to OpenAI’s FrontierMath Tier 1-3 suite and Sol leads at 89.0% against Fable 5’s 87.0%. Gemini 3.8 Flash undercuts both on price by roughly 80% while posting the highest GPQA Diamond score of the three. This comparison walks through the specs, the benchmark data, the real dollar cost, and which model actually fits which job, using only the numbers each company has published.
Three Reasoning Models, Three Release Windows
GPT-5.6 Sol is the flagship of OpenAI’s three-tier GPT-5.6 family, sitting above Terra and Luna in both price and reported benchmark scores. It shipped July 9, 2026, and OpenAI cut its API price on August 21, 2026, dropping input tokens from $5 to $4 per million and output tokens from $30 to $20 per million, a move Reuters covered as a direct response to pricing pressure from rival labs. That promotional rate holds at least through November 21, 2026, according to OpenAI’s own pricing documentation.
Claude Fable 5.1 arrived two months later, on September 1, 2026, alongside a smaller sibling called Claude Mythos 5.1. Anthropic kept the headline API price identical to the original Fable 5 ($10 per million input tokens, $50 per million output tokens) but cut cache-read pricing by 75%, from $1.00 to $0.25 per million tokens. The bigger change was architectural: reasoning between tool calls now happens inside dedicated thinking blocks that are interleaved automatically, with no beta header required to turn the behavior on.
Gemini 3.8 Flash is the newest of the three, landing September 3, 2026, alongside a cybersecurity-focused variant called Gemini 3.8 Flash Cyber. Google held pricing flat against the prior Gemini 3.7 Flash release, at $0.75 per million input tokens and $3.75 per million output tokens, and extended that introductory rate through December 31, 2026. It is also the only one of the three built explicitly as a mid-tier “Flash” model rather than a full flagship, which shows up directly in the cost comparisons later in this piece.
The timing is not a coincidence. All three labs are racing to claim the “best reasoning model” label heading into Q4 2026 enterprise budget cycles, and each release doubled as a pricing statement as much as a capability one. OpenAI’s August price cut on Sol came less than six weeks after Gemini 3.7 Flash undercut it on cost per token. Anthropic’s decision to hold Fable 5.1’s headline price flat while slashing cache-read cost by 75% reads as a bet that its agentic-reasoning lead on Terminal-Bench-Science justifies the premium, rather than competing on raw token price. Google, meanwhile, is playing a volume game: Gemini 3.8 Flash is explicitly a mid-tier model competing on price-to-performance rather than chasing the single highest benchmark score, and its GPQA Diamond result suggests that strategy is working better than its “Flash” branding might imply.
GPT-5.6 Sol, Claude Fable 5.1, and Gemini 3.8 Flash: Specs at a Glance
Before getting into benchmark scores, here is how the three models compare on the specs that determine whether they even fit your workload. This table pulls directly from each vendor’s own model documentation.
| Spec | GPT-5.6 Sol | Claude Fable 5.1 | Gemini 3.8 Flash |
|---|---|---|---|
| Developer | OpenAI | Anthropic | |
| Release date | July 9, 2026 | September 1, 2026 | September 3, 2026 |
| Model tier | Flagship | Flagship | Mid-tier “Flash” |
| API model ID | gpt-5.6-sol | claude-fable-5-1 | gemini-3.8-flash |
| Context window | ~1.05M tokens | 1M tokens | 1,048,576 tokens |
| Max output tokens | Not published | Not published | 65,536 tokens |
| Input price (per 1M tokens) | $4.00 | $10.00 | $0.75 |
| Output price (per 1M tokens) | $20.00 | $50.00 | $3.75 |
| Cached input price (per 1M tokens) | $0.40 | $0.25 (cache read) | Not published |
| Extended/tunable thinking | Configurable reasoning effort (low to max) | Automatic interleaved thinking blocks | Tunable thinking levels (low, medium, high) |
| Multimodal input | Text, image (per OpenAI docs) | Text, image, documents | Text, image, video, audio, PDF |
| Promotional pricing window | Through Nov 21, 2026 | Standard pricing (no promo listed) | Through Dec 31, 2026 |
Two things jump out immediately. First, all three models converge on roughly the same context window, around one million tokens, which is now table stakes for a 2026 reasoning-tier model rather than a differentiator. Second, the pricing spread is enormous: Gemini 3.8 Flash costs roughly a fifth of GPT-5.6 Sol’s input price and a fifteenth of Claude Fable 5.1’s. Whether that price gap is justified depends entirely on what the benchmarks say next.
Benchmark Breakdown: Who Actually Reasons Best
No single model wins every reasoning benchmark. That’s the headline finding once you line up the published scores side by side. GPT-5.6 Sol dominates OpenAI’s own competition-math suite and the ARC Prize’s abstraction tests. Claude Fable 5.1 leads on agentic scientific research and software-engineering benchmarks. Gemini 3.8 Flash posts the top graduate-level science score despite costing a fraction of the other two. Here’s the full comparison, pulled from OpenAI’s GPT-5.6 announcement, Anthropic’s Fable 5.1 launch coverage, and the ARC Prize leaderboard.
| Benchmark | GPT-5.6 Sol | Claude Fable 5.1 / 5 | Gemini 3.8 Flash | Leader |
|---|---|---|---|---|
| GPQA Diamond (graduate science) | 94.6% | 92.6% (Fable 5) | 95.3% | Gemini 3.8 Flash |
| FrontierMath Tier 1-3 (v2) | 89.0% | 87.0% (Fable 5) | Not published | GPT-5.6 Sol |
| FrontierMath Tier 4 (v2) | 83.0% | 87.8% (Fable 5) | Not published | Claude Fable 5 |
| ARC-AGI-2 | 92.5% (max reasoning) | Not published | Not published | GPT-5.6 Sol |
| ARC-AGI-3 | 7.78% | Not published | Not published | GPT-5.6 Sol |
| Terminal-Bench-Science 0.1 | 22.4% | 52.6% | Not published | Claude Fable 5.1 |
| SWE Pro (software engineering) | Not published | 81.2% | Not published | Claude Fable 5.1 |
| Artificial Analysis Coding Agent Index | 80 | Not published | Not published | GPT-5.6 Sol |
Two caveats matter here. Anthropic has not published a Fable 5.1-specific FrontierMath or GPQA score in its launch materials, so several rows use the prior Fable 5 figures as the closest available comparison; where that happens, the table flags it. And ARC-AGI-3, the newest and hardest tier of the ARC Prize suite, remains largely unsolved by every frontier model tested so far. GPT-5.6 Sol’s 7.78% is the best publicly reported score in this comparison, not a sign that the benchmark is close to saturated.
GPQA Diamond and FrontierMath: The Math and Science Gap
GPQA Diamond tests graduate-level questions in biology, physics, and chemistry that are deliberately hard to answer by pattern-matching against training data. All three models clear 92%, which says more about how far frontier reasoning has come in a year than it does about which model to pick. Gemini 3.8 Flash’s 95.3% is the highest reported score among the three, according to a September 2026 frontier benchmark aggregator that tracks all major labs’ releases. That is a notable result for a model priced as a mid-tier “Flash” product rather than a flagship.
FrontierMath tells a more layered story. OpenAI’s own GPT-5.6 announcement breaks the suite into Tier 1-3 (harder undergraduate and early-graduate problems) and Tier 4 (research-level competition math). Sol leads Tier 1-3 at 89.0%, but on Tier 4, Anthropic’s Fable 5 line pulls ahead at 87.8% against Sol’s 83.0%. That split suggests OpenAI has optimized Sol for the broad middle of hard math problems, while Anthropic’s training has pushed further into the genuinely research-grade tier. If your use case involves formal math research or proof verification, that Tier 4 gap is the number to weight most heavily, not the headline GPQA score.
ARC-AGI-2 and ARC-AGI-3: Abstract Reasoning Still Breaks All Three
The ARC Prize benchmark suite tests abstract pattern recognition on novel puzzle-style tasks that can’t be solved by memorizing training data. It is widely treated as one of the more honest proxies for general reasoning ability precisely because it resists brute-force scaling. GPT-5.6 Sol reaches 92.5% on ARC-AGI-2 at its maximum reasoning setting, a score verified directly by the ARC Prize organization rather than self-reported by OpenAI. Anthropic and Google have not published comparable ARC-AGI-2 scores for Fable 5.1 or Gemini 3.8 Flash in their launch materials, which is itself informative: none of the three companies has made ARC-AGI performance a headline marketing number the way FrontierMath or GPQA scores are.
That silence is worth reading carefully rather than filling in with assumptions. ARC-AGI scores are independently verified by a third-party organization rather than self-reported in a vendor blog post, which makes them harder to game but also means a company can simply decline to submit a model for testing without facing the same scrutiny it would for skipping a benchmark it controls. Until Anthropic or Google publishes a verified ARC-AGI-2 result for Fable 5.1 or Gemini 3.8 Flash, treat GPT-5.6 Sol’s lead on this specific test as a claim about the only model in this comparison with public, third-party-verified abstraction scores, not proof that the other two are necessarily weaker.
ARC-AGI-3, the newest and by far hardest tier, remains a different story entirely. Sol scores just 7.78%, and OpenAI’s own Terra and Luna variants score 0.80% and 0.18% respectively. For context, Claude Opus 4.8 reportedly scores around 1.5% and GPT-5.5 around 0.43% on the same test, according to a September 2026 benchmark aggregator that tracks cross-vendor ARC Prize results. Sol did notch a symbolic milestone, becoming, according to the ARC Prize leaderboard, the first model to win a public ARC-AGI-3 game outright. But the single-digit percentages across the board are a reminder that “reasoning-tier” branding describes marketing tiers, not solved problems. If your evaluation criteria include truly novel abstract reasoning rather than well-represented problem types, none of these three models should be treated as reliable yet.
Terminal-Bench-Science and SWE Pro: Agentic Reasoning in Practice
This is where the comparison gets genuinely interesting, because it’s the one place the three vendors’ benchmark choices actually overlap enough to produce a direct head-to-head. Terminal-Bench-Science 0.1 is an agentic benchmark that asks a model to carry out multi-step scientific research tasks inside a terminal environment: formulating hypotheses, running analysis scripts, and iterating on results without constant human steering. Anthropic’s own launch data, reported by MarkTechPost on September 1, 2026, shows Claude Fable 5.1 scoring 52.6% against Claude Opus 5’s 29.0%, the older Claude Fable 5’s 24.7%, and GPT-5.6 Sol’s 22.4%. That’s a 30.2-point gap between the top and bottom score on the same test, and it’s the single largest gap in this entire comparison.
The pattern holds on SWE Pro, a software-engineering benchmark modeled on the SWE-bench family of real-world GitHub issue resolution tasks, where Claude Fable 5.1 leads at 81.2%. GPT-5.6 Sol counters with the top score on the Artificial Analysis Coding Agent Index at 80, a separate composite benchmark that weights coding-agent behavior across a broader task set rather than a single suite. Put together, these numbers suggest Anthropic’s post-training work on Fable 5.1 leaned hard into long-horizon agentic tasks specifically, while OpenAI’s Sol optimized more for raw math and abstract reasoning benchmarks. Neither approach is objectively better; they’re different bets on what “reasoning” should mean in production.
How Extended Thinking Works in Each Model
All three vendors now expose a controllable reasoning-effort setting, but the implementation differs in ways that affect both latency and cost.
- GPT-5.6 Sol exposes discrete reasoning levels, described in OpenAI’s own benchmark tables as settings like “Thinking Level: High” or “max reasoning.” Sol’s headline ARC-AGI-2 score of 92.5% is explicitly reported “at max reasoning,” meaning the default configuration likely scores lower. Developers pick the effort level per request through the API.
- Claude Fable 5.1 handles reasoning differently: Anthropic’s own documentation describes reasoning between tool calls as now happening automatically inside dedicated thinking blocks, interleaved without requiring a beta header. Developers can still raise or lower a default effort setting, but the thinking itself is structured and separated from the visible response text by default, a change from how earlier Claude models exposed reasoning as plain text.
- Gemini 3.8 Flash exposes tunable thinking levels of low, medium, or high directly as an API parameter, according to Google’s own developer documentation. Google markets 3.8 Flash as engineered for long-horizon software engineering and autonomous agents specifically because of this tunable reasoning layer, paired with the lowest price of the three.
In practice, higher thinking levels increase both latency and token consumption across all three models, since the reasoning tokens are typically billed at the same rate as output tokens even when they’re hidden from the final response. That makes the effort-level setting a direct cost lever, not just a quality one, which is worth testing explicitly before committing to a default in production.
The practical difference shows up fastest in latency-sensitive applications. A customer support triage bot running at Gemini 3.8 Flash’s low thinking level will respond in a fraction of the time a high-effort GPT-5.6 Sol call takes on the same prompt, but it will also miss multi-step reasoning failures that the higher-effort call would have caught. Teams building latency-sensitive products should treat the thinking-level parameter as a per-endpoint decision rather than an account-wide default: a support bot’s first-response classifier can run at low effort, while the same product’s escalation-handling agent might need medium or high effort to avoid compounding errors across a longer conversation.
| Reasoning control | GPT-5.6 Sol | Claude Fable 5.1 | Gemini 3.8 Flash |
|---|---|---|---|
| Control mechanism | Discrete reasoning-effort levels (low to max) | Default effort setting plus automatic thinking blocks | API parameter: low, medium, or high |
| Thinking visible in output | Configurable | Structured thinking blocks, separated from response text | Configurable per thinking level |
| Billed as | Output-rate tokens | Output-rate tokens | Output-rate tokens |
| Headline benchmark used at | Max reasoning | Default effort | Not specified in launch docs |
Pricing Per Million Tokens: The Real Cost Table
Here is the full pricing picture, including OpenAI’s cheaper Terra and Luna tiers for context, since teams evaluating Sol often end up choosing a lower tier once they see the price difference against Gemini 3.8 Flash.
| Model | Input $/1M tokens | Output $/1M tokens | Cached input $/1M | Cost for 1M in + 1M out |
|---|---|---|---|---|
| GPT-5.6 Sol | $4.00 | $20.00 | $0.40 | $24.00 |
| GPT-5.6 Terra | $2.50 | $15.00 | Not published | $17.50 |
| GPT-5.6 Luna | $1.00 | $6.00 | Not published | $7.00 |
| Claude Fable 5.1 | $10.00 | $50.00 | $0.25 (cache read) | $60.00 |
| Gemini 3.8 Flash | $0.75 | $3.75 | Not published | $4.50 |
At the flagship tier, Claude Fable 5.1 costs 2.5 times more than GPT-5.6 Sol per combined million tokens, and more than 13 times what Gemini 3.8 Flash charges for the same volume. That gap only makes sense if Fable 5.1’s Terminal-Bench-Science and SWE Pro advantages translate into fewer retries, less human correction, or better first-pass output on the specific task you’re running. For high-volume, lower-complexity workloads, the price differential is hard to justify against Gemini 3.8 Flash’s GPQA-leading score at a fifth of Sol’s price and a fifteenth of Fable 5.1’s.
Real-World Cost Examples: What a Workload Actually Costs
Benchmark percentages are abstract until you attach them to a monthly bill. Here’s what a moderately heavy reasoning workload costs across all three models: 500,000 input tokens (a batch of documents, code files, or research data) and 50,000 output tokens (a generated report, patch, or analysis) per run, executed 100 times a month, a realistic load for a mid-size engineering or research team running a daily automated agent.
| Model | Cost per run | Monthly cost (100 runs) | Annual cost |
|---|---|---|---|
| GPT-5.6 Sol | $3.00 | $300.00 | $3,600.00 |
| Claude Fable 5.1 | $7.50 | $750.00 | $9,000.00 |
| Gemini 3.8 Flash | $0.56 | $56.25 | $675.00 |
Over a year, the gap between the cheapest and most expensive option in this example is more than $8,300, on identical token volume. That doesn’t mean Gemini 3.8 Flash is the right call for every team; if a research pipeline lives or dies on the Terminal-Bench-Science-style agentic reasoning where Fable 5.1 posted a 30-point lead, the extra cost may pay for itself many times over in reduced human review time. But for workloads where all three models clear your accuracy bar, the annual difference is significant enough to warrant a real evaluation rather than defaulting to the most expensive flagship.
Five Real-World Use Cases: Which Model Fits
Scientific research agents
A biotech team running an autonomous literature-review-and-hypothesis-generation pipeline is exactly the workload Terminal-Bench-Science was built to measure. With Claude Fable 5.1 scoring more than double GPT-5.6 Sol on that benchmark, this is the clearest case in this comparison where paying the 2.5x price premium over Sol is defensible on the data alone. A pharma research group running daily automated literature scans across preprint servers, for instance, would likely see the benchmark gap translate directly into fewer flagged hypotheses that turn out to misread a source paper, which matters more than the per-token cost when a single missed contraindication carries real downstream risk.
Competition-math and Tier 1-3 problem solving
For teams building math-tutoring products or automated grading systems that deal mostly with advanced-but-not-research-level problems, GPT-5.6 Sol’s 89.0% on FrontierMath Tier 1-3 is the strongest published score among the three, at roughly a third of Fable 5.1’s price. An ed-tech platform grading advanced-placement or early-university coursework sits squarely in this tier, where Sol’s combination of leading accuracy and a mid-range price point is likely to beat Fable 5.1 on cost-per-graded-submission without a meaningful accuracy tradeoff.
Research-grade math and formal proof work
If the workload sits specifically at FrontierMath Tier 4, competition-level problems that stump most human mathematicians, Anthropic’s Fable line holds the published lead at 87.8% against Sol’s 83.0%. This is a narrower use case than general math tutoring, and it’s worth confirming Fable 5.1 (rather than the older Fable 5 figure used here) holds that lead before committing, since Anthropic has not republished this specific score under the 5.1 label. University research groups and quant teams verifying proof-heavy work are the most likely to notice this gap in practice, since Tier 4 problems are specifically selected to sit beyond what most working mathematicians solve unaided.
Long-document compliance and contract review
All three models now handle roughly a million tokens of context, but Gemini 3.8 Flash adds native PDF, video, and audio input alongside text, per Google’s own developer documentation. Combined with its price of $0.75 per million input tokens, it’s the strongest fit for high-volume document review where cost per page matters more than winning the hardest reasoning benchmark. A legal operations team processing thousands of vendor contracts a month, for example, would spend roughly $56 processing 500,000 tokens of contracts through Gemini 3.8 Flash versus $300 through GPT-5.6 Sol for the same volume, a gap that compounds fast at enterprise scale.
Enterprise coding agents and SWE automation
Teams automating GitHub issue resolution or CI-triage agents have two strong options here: Claude Fable 5.1 leads published SWE Pro scores at 81.2%, while GPT-5.6 Sol leads the Artificial Analysis Coding Agent Index at a score of 80 on a different composite benchmark. Because these are two different evaluation suites rather than the same test, the honest answer is to pilot both against your own repository’s issue backlog before standardizing.
Cost-sensitive customer-facing deployments
For chatbots, internal search assistants, or any product serving reasoning-tier quality to a large user base, Gemini 3.8 Flash’s GPQA Diamond lead at 95.3%, combined with pricing roughly a fifteenth of Fable 5.1’s, makes it the default starting point. Teams should still benchmark their own eval set, but the price gap alone rules out the two flagships for most consumer-scale deployments.
Independent Benchmark Aggregators: Cross-Checking the Vendor Claims
Vendor-published benchmarks are useful, but they’re also marketing documents, chosen because the model in question performs well on them. Third-party aggregators that run the same test suite across every frontier release, rather than letting each lab report its own preferred subset, are a better sanity check before committing budget to one model. A September 4, 2026 aggregator that tracks frontier benchmark scores across labs lists GPT-5.6 Sol leading ARC-AGI-2 at 92.5%, Gemini 3.8 Flash leading GPQA at 95.3%, and Claude Fable 5.1 leading SWE Pro at 81.2%, matching the vendor-reported numbers used throughout this comparison rather than contradicting them. That agreement across an independent source and each company’s own launch materials is a reasonable signal that the headline numbers in this piece are not cherry-picked outliers.
What the aggregator data does not resolve is FrontierMath and Terminal-Bench-Science scores specific to Claude Fable 5.1 rather than the prior Fable 5 release, since Anthropic’s own launch materials for 5.1 focus almost entirely on the Terminal-Bench-Science result and cache pricing, without republishing FrontierMath or GPQA numbers under the 5.1 label. Until an independent aggregator runs Fable 5.1 specifically against those two suites, treat the Fable 5 figures used in the tables above as the closest available estimate, not a confirmed Fable 5.1 score.
Safety, Availability, and Enterprise Considerations
Benchmark scores tell you what a model can do in a controlled test; they say less about how it behaves once wired into a production system with real users and real data. All three vendors ship these reasoning-tier models with some form of safety documentation, though the depth and format differs. Anthropic pairs Claude Fable 5.1’s release with the same enterprise-tier access controls, usage-credit tracking, and premium-seat inclusion used for the prior Fable 5 release, according to Anthropic’s own plan documentation, meaning teams already running Fable 5 on Anthropic’s Max or Pro plans get access to 5.1 without a separate procurement step. OpenAI’s GPT-5.6 family ships with a system card covering the Sol, Terra, and Luna variants together, and the pricing structure (short-context versus long-context rates, cached versus uncached input) is documented in the same official pricing table referenced throughout this piece, which matters for finance teams trying to forecast spend before a full migration.
Google’s Gemini 3.8 Flash and its Cyber-focused sibling both launched through the standard Gemini API and Google AI Studio channels, with the same introductory pricing applied across Google’s Enterprise Agent Platform, according to Google’s own developer documentation. For regulated industries, the practical question is less about raw benchmark performance and more about audit logging, data residency, and retention policy, none of which are captured in a GPQA or SWE Pro score. Before standardizing on any of the three models for a regulated workload, confirm the specific enterprise agreement terms directly with the vendor rather than assuming benchmark-tier parity implies compliance-tier parity.
Migration Guide: Switching Your Stack to a Reasoning-Tier Model
Moving a production pipeline from an older model, whether that’s GPT-5.5, Claude Opus 4.8, or Gemini 3.1 Pro, to one of these three reasoning-tier releases isn’t a drop-in swap. Here’s a practical sequence for making the switch without breaking cost budgets or output quality.
- Audit current token volume by workload. Pull 30 days of input and output token counts from your existing provider’s usage dashboard, split by use case, before estimating new costs.
- Build a small, representative eval set. Pull 20-50 real production examples per workload, including edge cases that previously required retries or human correction.
- Run all three models cold, at default thinking/effort settings, against the eval set before tuning anything.
- Score accuracy and failure modes separately, not just a pass rate. Note whether errors are factual, formatting, or reasoning-depth failures, since each points to a different fix.
- Test each model’s extended-thinking parameter at low, medium, and high settings on the subset of examples that failed at default settings.
- Recalculate cost per workload using your actual token counts and the pricing table above, not published benchmark-run costs.
- Update model IDs and SDK calls. Swap the API model identifier (gpt-5.6-sol, claude-fable-5-1, or gemini-3.8-flash) and confirm your SDK version supports the new thinking-block or reasoning-effort parameters.
- Enable prompt caching where available. GPT-5.6 Sol’s cached input rate is 90% below its base input price; Claude Fable 5.1’s cache-read rate is 97.5% below base. Both are worth architecting for if your prompts share a stable system message or document context.
- Run a shadow deployment. Route a small percentage of live traffic to the new model while keeping the old one as the default, comparing latency and output quality under real load.
- Set a hard cost ceiling and alerting before full cutover, since reasoning-tier models can silently increase spend through higher thinking-token consumption even when the per-token price looks favorable on paper.
A short code example illustrates the actual change at the SDK level for a Python-based pipeline switching from an older Claude model to Fable 5.1:
# Before: previous-generation model call
response = client.messages.create(
model="claude-opus-4-8",
max_tokens=4096,
messages=[{"role": "user", "content": prompt}]
)
# After: Claude Fable 5.1 with explicit thinking effort
response = client.messages.create(
model="claude-fable-5-1",
max_tokens=4096,
thinking={"type": "enabled", "budget_tokens": 8000},
messages=[{"role": "user", "content": prompt}]
)Pros and Cons of Each Model
| Model | Pros | Cons |
|---|---|---|
| GPT-5.6 Sol | Leads FrontierMath Tier 1-3 and ARC-AGI-2; recent price cut to $4/$20; leads Artificial Analysis Coding Agent Index | Trails Fable 5.1 badly on Terminal-Bench-Science (22.4% vs 52.6%); max output tokens not published; mid-price tier |
| Claude Fable 5.1 | Dominant on agentic science and SWE Pro benchmarks; structured automatic thinking blocks; 75% cheaper cache reads than Fable 5 | Highest price of the three at $10/$50 per million; no published FrontierMath Tier 1-3 or GPQA score specific to 5.1; Mythos 5.1 sibling has almost no public benchmark data yet |
| Gemini 3.8 Flash | Highest GPQA Diamond score (95.3%); cheapest by a wide margin at $0.75/$3.75; native video, audio, and PDF input; promo pricing through end of 2026 | No published FrontierMath, ARC-AGI, or Terminal-Bench-Science score to compare against the other two; “Flash” tier positioning may signal a smaller model than the two flagships |
Which Model Fits Which Use Case: Recommendation Table
| Priority | Recommended model | Why |
|---|---|---|
| Agentic scientific research | Claude Fable 5.1 | 52.6% on Terminal-Bench-Science, more than double GPT-5.6 Sol’s 22.4% |
| Advanced but non-research math | GPT-5.6 Sol | 89.0% on FrontierMath Tier 1-3, the highest published score |
| Research-grade math (Tier 4) | Claude Fable 5 / 5.1 | 87.8% on FrontierMath Tier 4 versus Sol’s 83.0% |
| Novel abstract reasoning | GPT-5.6 Sol | 92.5% on ARC-AGI-2, first model to win a public ARC-AGI-3 game |
| Software engineering agents | Claude Fable 5.1 | 81.2% on SWE Pro, the highest published score in this comparison |
| High-volume, cost-sensitive deployment | Gemini 3.8 Flash | $4.50 per combined million tokens versus $24 (Sol) and $60 (Fable 5.1) |
| Long-document, multimodal review | Gemini 3.8 Flash | Native PDF, video, and audio input at the lowest price per token |
The Verdict: A Data-Backed Pick, Not a Single Winner
There is no single best model among GPT-5.6 Sol, Claude Fable 5.1, and Gemini 3.8 Flash, and any comparison that claims otherwise is ignoring the benchmark data. Each vendor optimized for a different slice of reasoning work. OpenAI pushed GPT-5.6 Sol toward math and abstract-pattern benchmarks, where it leads FrontierMath Tier 1-3 at 89.0% and ARC-AGI-2 at 92.5%. Anthropic pushed Claude Fable 5.1 toward long-horizon agentic tasks, where its 52.6% on Terminal-Bench-Science more than doubles Sol’s score and its 81.2% on SWE Pro leads published coding-agent benchmarks. Google pushed Gemini 3.8 Flash toward efficiency, landing the highest GPQA Diamond score of the three at 95.3% while charging roughly a fifth of Sol’s price and a fifteenth of Fable 5.1’s.
If forced to pick a single starting point for a team with no existing commitment to one vendor, the data points toward Gemini 3.8 Flash as the default first test: it posts a competitive or leading score on the one benchmark all three models share (GPQA Diamond) at a fraction of the cost, which makes it the cheapest hypothesis to disprove. Teams should then escalate to GPT-5.6 Sol for math-heavy or abstract-reasoning workloads, and to Claude Fable 5.1 specifically when the task looks like Terminal-Bench-Science or SWE Pro, meaning long-horizon, multi-step agentic work where the 30-point benchmark gap is large enough to justify the premium. Run your own eval set before committing either way. Published benchmarks measure the test, not your production data.
| Category | Benchmark wins (of 8 tracked) | Price for 1M in + 1M out |
|---|---|---|
| GPT-5.6 Sol | 4 (FrontierMath T1-3, ARC-AGI-2, ARC-AGI-3, Coding Agent Index) | $24.00 |
| Claude Fable 5.1 / 5 | 3 (FrontierMath T4, Terminal-Bench-Science, SWE Pro) | $60.00 |
| Gemini 3.8 Flash | 1 (GPQA Diamond) | $4.50 |
Counting raw benchmark wins puts GPT-5.6 Sol ahead on volume, but that scorecard undersells Claude Fable 5.1’s position: its three wins are concentrated in exactly the agentic and software-engineering categories that map most directly to autonomous production workloads, while two of Sol’s four wins (ARC-AGI-2 and ARC-AGI-3) are on a benchmark where the entire industry still scores in the single digits at the hardest tier. Weigh the wins by relevance to your actual workload, not just by count.
Frequently Asked Questions
Is GPT-5.6 Sol better than Claude Fable 5.1?
It depends on the task. GPT-5.6 Sol leads on FrontierMath Tier 1-3 (89.0% vs 87.0%) and ARC-AGI-2 (92.5%), while Claude Fable 5.1 leads on Terminal-Bench-Science (52.6% vs 22.4%) and SWE Pro (81.2%). Neither model wins across every published benchmark.
How much does Gemini 3.8 Flash cost compared to GPT-5.6 Sol?
Gemini 3.8 Flash costs $0.75 per million input tokens and $3.75 per million output tokens, versus GPT-5.6 Sol’s $4.00 and $20.00. For a combined one million input and one million output tokens, Gemini 3.8 Flash costs $4.50 against Sol’s $24.00, roughly a fifth of the price.
What is Claude Mythos 5.1, and how does it compare?
Claude Mythos 5.1 launched alongside Claude Fable 5.1 on September 1, 2026, as a companion release from Anthropic. As of this comparison, Anthropic has not published detailed independent benchmark scores or pricing specifically for Mythos 5.1, so it is not included in the head-to-head benchmark tables above.
Which model has the largest context window?
All three are effectively tied around one million tokens: GPT-5.6 Sol at roughly 1.05 million, Claude Fable 5.1 at one million, and Gemini 3.8 Flash at exactly 1,048,576 tokens. Context window size is no longer a meaningful differentiator among these three releases.
Why does GPT-5.6 Sol score so much lower on Terminal-Bench-Science?
Anthropic’s own launch data shows GPT-5.6 Sol scoring 22.4% against Claude Fable 5.1’s 52.6% on Terminal-Bench-Science 0.1. The benchmark specifically tests multi-step agentic scientific research, which appears to be an area Anthropic prioritized in Fable 5.1’s post-training, based on the size of the published gap relative to other benchmarks like FrontierMath, where the two models are much closer.
Can these models solve ARC-AGI-3 reliably?
No. GPT-5.6 Sol posts the highest reported score among current frontier models at 7.78%, with OpenAI’s own Terra and Luna variants scoring below 1%. ARC-AGI-3 remains largely unsolved across the industry as of September 2026, despite Sol reportedly becoming the first model to win a public ARC-AGI-3 game.
Do these models support prompt caching to reduce cost?
Yes, for GPT-5.6 Sol and Claude Fable 5.1. Sol’s cached input price is $0.40 per million tokens, 90% below its $4.00 base input rate. Fable 5.1’s cache-read price is $0.25 per million tokens, a 75% cut from Fable 5’s prior cache-read pricing and 97.5% below its $10.00 base input rate. Google has not published a specific cached-input rate for Gemini 3.8 Flash in the documentation reviewed for this comparison.
Should I switch my whole pipeline to one of these models immediately?
No. Run a representative eval set against your own production data before switching, since published benchmark scores measure performance on the benchmark’s specific task distribution, not necessarily yours. The migration guide above outlines a shadow-deployment approach that limits risk while still capturing the accuracy and cost differences that matter for your workload.
