Three flagship chatbots and one very late arrival all shipped inside a single three-week window this September, and for the first time in months the fight between them isn’t really about who writes the cleanest code. OpenAI’s GPT-6 Astra, Anthropic’s Claude Fable 5.1, Google’s Gemini 3.8 Flash, and xAI’s Grok 4.7 have each published (or had leaked) reasoning-benchmark numbers that tell a messier story than the marketing decks suggest. When you line up ChatGPT vs Claude vs Gemini vs Grok on graduate-level science questions, factual grounding tests, and real-world agentic tasks, no single model wins everything, and the gaps between them are large enough to matter for anyone picking a daily driver.
This comparison pulls scores from the Artificial Analysis Intelligence Index, Google’s FACTS Grounding leaderboard, OpenAI’s SimpleQA benchmark, and independent agentic-coding trackers, then lines them up against what each model actually costs to run and subscribe to. The goal isn’t to crown an “ultimate” winner — it’s to show, with numbers, which of these four you should actually be paying for depending on what you do with a chatbot every day.
The framing matters because the four labs are no longer competing on the same axis. A year ago, “which chatbot is smartest” was a reasonably coherent question with a reasonably clear answer on any given month. Now each provider is optimizing for a different slice of the market at the same time: OpenAI is chasing grounding and long-document accuracy, Anthropic is chasing agentic reliability for coding workflows, Google is chasing cost-per-token at massive scale, and xAI is chasing a value position on agentic tasks specifically. That divergence is the real story behind the numbers below, and it’s why a simple ranking table would flatten out information that actually matters for choosing between them.
What Shipped in September 2026: Astra, Fable 5.1, Flash, and Grok 4.7
The timing here is almost comedic. Anthropic released Claude Fable 5.1 on September 1, 2026, generally available on every platform on day one with no staged preview. Google followed a day later with Gemini 3.8 Flash reaching general availability on September 2. OpenAI answered on September 3–4 with GPT-6 Astra, its self-described “best model for software engineering to date” and the first OpenAI model rated Critical for cybersecurity capability. xAI took the longest to respond, shipping Grok 4.7 on September 21 — just two days before this article was published, which means real-world usage data on it is still thin.
That compressed release cycle isn’t an accident. Google’s Flash line alone moved through three versions in 43 days — 3.6 Flash on July 21, 3.7 Flash on August 13, and 3.8 Flash on September 2 — according to release trackers cited by Kaggle’s FACTS Grounding leaderboard. Anthropic and OpenAI are on a similar cadence: Claude Opus 5 shipped July 24, Claude Sonnet 5 landed June 30, and GPT-5.6 (Sol, Terra, Luna) arrived July 9 before Astra effectively replaced all three tiers as OpenAI’s recommended default in September.
For anyone comparing ChatGPT vs Claude vs Gemini vs Grok right now, the practical takeaway is that “current” has a shelf life measured in weeks, not months. Astra, Fable 5.1, and Gemini 3.8 Flash are all less than a month old at publication, and Grok 4.7 is barely three weeks old. Benchmark numbers below reflect the freshest published data as of September 23, 2026, and some entries — particularly for Grok 4.7 and Gemini 3.8 Pro — are still incomplete because the labs haven’t published every score yet.
This pace also changes how you should read any comparison, including this one. A model that trails today on a specific benchmark can close the gap with a point release inside a month, as Gemini’s Flash line has repeatedly demonstrated by shipping three versions in six weeks. The practical advice for readers isn’t to memorize today’s scores as permanent fact — it’s to understand which benchmark categories map to your actual work, so that when the next round of releases lands, you know which numbers to check first rather than reacting to a headline Intelligence Index score alone.
Specs at a Glance: The Full Comparison Table
Before getting into what the benchmarks measure, here’s the full spec sheet side by side. Where a lab hasn’t published a figure, it’s marked as such rather than estimated, per our sourcing standards.
| Spec | GPT-6 Astra (OpenAI) | Claude Fable 5.1 (Anthropic) | Gemini 3.8 Flash (Google) | Grok 4.7 (xAI) |
|---|---|---|---|---|
| Release date | Sept 3–4, 2026 | Sept 1, 2026 | Sept 2, 2026 | Sept 21, 2026 |
| Context window | 1,050,000 tokens (922,000 max input) | 1,000,000 tokens | 1,048,576 tokens | 500,000 tokens |
| Max output tokens | 128,000 | Not publicly disclosed | Not publicly disclosed | Not publicly disclosed |
| API input price (per 1M tokens) | $10.00 | $10.00 | $0.75 (intro rate through Dec 31, 2026) | $2.00 (below 200K context) |
| API output price (per 1M tokens) | $50.00 | $50.00 | $3.75 (intro rate through Dec 31, 2026) | $6.00 (below 200K context) |
| Cached input discount | $1.00/1M (90% off) | $0.25/1M (75%+ cut vs Fable 5) | Not publicly disclosed | Not publicly disclosed |
| Artificial Analysis Intelligence Index v4.1.1 | 53 | 53 | Not yet published on this index | 46 (per AI Weekly’s Sept 21 tracking) |
| GPQA Diamond (graduate science) | 96.1% | Not yet published | Not yet published | Not yet published |
| FACTS Grounding | 82.0% | Not yet published | 73.1% | Not yet published (predecessor Grok 4.5: 74.5%) |
| SimpleQA Verified | 75.6% | 70.8% | 69.4%–69.7% | Not yet published |
| Terminal-Bench 4.0 (agentic tasks) | 57.9% | 55.8% | 19.1% | Not yet published |
| DeepSWE v1.1 | 74.1% | 67.4% | Not yet published | 71% (per xAI-reported figure) |
| Entry consumer subscription | Plus, $20/month | Pro, $20/month ($17/month billed annually) | AI Pro (Gemini Advanced), $19.99/month | SuperGrok, $30/month |
| Top consumer tier | Pro, $200/month (20x usage) | Max 20x, $200/month | AI Ultra 20x, $199.99/month | SuperGrok Heavy, roughly $300/month |
A few things jump out immediately. Gemini 3.8 Flash is priced roughly 13 times cheaper per input token than GPT-6 Astra or Claude Fable 5.1, which explains why Google can afford to ship a new Flash variant every few weeks. Grok 4.7 sits in between on price but trails badly on the Intelligence Index — a gap worth unpacking, since xAI’s own DeepSWE number (71%) suggests the model isn’t weak everywhere, just in different places than its rivals.
How These Benchmarks Actually Work
Before trusting any single number, it helps to know what it’s actually testing. The Artificial Analysis Intelligence Index v4.1.1 is a composite score built from nine separate evaluations: GDPval-AA v2 (economically valuable real-world tasks), Tau-3 Banking (customer-service and financial workflows), Terminal-Bench v2.1 (command-line and agentic tasks), SciCode (scientific programming), Humanity’s Last Exam (expert-level questions spanning dozens of fields), GPQA Diamond, CritPt (physics reasoning), AA-Omniscience (broad factual knowledge), and AA-LCR (long-context retrieval). It’s a single number, but it’s built from very different skills, which is why a model can score well overall while bombing a specific sub-test.
FACTS Grounding, published by Google and hosted on Kaggle, measures something narrower: whether a model’s answer is fully supported by a provided source document, without adding unsupported claims. SimpleQA Verified, an OpenAI benchmark also tracked on Kaggle, tests short-answer factual recall against a fixed set of hard trivia-style questions with verifiable single answers. Neither one measures raw intelligence, reasoning depth, or coding skill — they measure whether the model tells the truth when it’s supposed to, which is a genuinely different axis from the Intelligence Index composite.
Terminal-Bench 4.0 and DeepSWE v1.1 sit in a third category entirely: agentic execution. These benchmarks drop a model into a sandboxed environment and measure whether it can complete multi-step technical tasks autonomously — configuring software, debugging a broken build, navigating a file system — rather than just answering a question in one shot. A model that reasons brilliantly in a single turn can still fail badly here if it can’t recover from its own mistakes across a long task chain, which is exactly what happened to Gemini 3.8 Flash’s Terminal-Bench score.
Artificial Analysis Intelligence Index: Who Scores Highest
On the composite Intelligence Index, Claude Fable 5.1 and GPT-6 Astra are level on the composite Intelligence Index at 53, according to figures corroborated across multiple trackers referencing Artificial Analysis’s own benchmarking writeup. That’s a notable result given OpenAI’s marketing push around Astra as its new flagship — on this particular composite, Anthropic’s model is level with GPT-6 Astra, not ahead, and Fable 5.1 also edges out Anthropic’s own previous top model, Claude Opus 5, which scored 63.1.
Grok 4.7 is the outlier. A same-day tracking report from AI Weekly, published on the day of Grok 4.7’s launch, put its Intelligence Index score at 46 — roughly 7 points behind both Claude Fable 5.1 and GPT-6 Astra. That’s a meaningful gap on a scale where the leading cluster of frontier models sits in the 60s. Google’s Gemini 3.8 Flash hasn’t been scored on this specific index version as of publication, which is itself notable: Google has leaned on FACTS Grounding and its own internal benchmarks more than the Artificial Analysis composite for its Flash-tier releases.
Read narrowly, ChatGPT vs Claude vs Gemini vs Grok on pure composite intelligence right now looks like a two-model race between Astra and Fable 5.1, with Grok trailing by a wide margin and Gemini’s flagship reasoning tier simply unmeasured on this particular yardstick. That doesn’t mean Grok 4.7 is a bad model — its DeepSWE score of 71% is competitive with Astra’s 74.1% — but it does mean xAI’s newest release isn’t winning on general-purpose reasoning breadth the way its release messaging implied.
Graduate-Level Science Reasoning: GPQA Diamond
GPQA Diamond is one of the harder single benchmarks in circulation — it’s built from PhD-level questions in biology, physics, and chemistry that are deliberately written so that non-experts (including people using search engines) can’t reliably guess the answer. GPT-6 Astra posts a 96.1% score here, per data aggregated on Artificial Analysis’s GPT-6 Astra model page, which is an exceptionally high result on a test designed to resist exactly this kind of saturation.
Unfortunately, this is one of the areas where the comparison breaks down for the other three models. As of this writing, Claude Fable 5.1, Gemini 3.8 Flash, and Grok 4.7 don’t have independently published GPQA Diamond scores available through the trackers used for this article. That’s a genuine gap in the public record rather than a sign those models are weak here — Anthropic and Google have historically posted strong GPQA numbers on previous model generations — but until the labs publish updated figures for their newest releases, a direct four-way GPQA comparison for the current generation isn’t possible with verified data. Treat Astra’s 96.1% as a confirmed high-water mark rather than proof that it’s automatically ahead of the others on this specific test.
Grounding and Factual Reliability: FACTS and SimpleQA
This is where the four-way comparison gets genuinely interesting, because it’s the one category with usable data across three of the four models. On FACTS Grounding, GPT-6 Astra leads at 82.0%, with Gemini 3.8 Flash at 73.1% and Grok’s predecessor, Grok 4.5, at 74.5% (Grok 4.7 hasn’t posted an updated figure yet). Google’s own prior Flash release, Gemini 3.7 Flash, actually scored higher than 3.8 Flash on this specific test — 75.5% versus 73.1% — which is a reminder that newer isn’t automatically better on every axis, especially when a release like 3.8 Flash is explicitly a post-training iteration on the same base model rather than a full retrain.
On SimpleQA Verified, the ordering shifts slightly: GPT-6 Astra again leads at 75.6%, Claude Fable 5.1 follows at 70.8%, and Gemini 3.8 Flash trails at somewhere between 69.4% and 69.7% depending on whether you use the OpenAI-hosted Kaggle leaderboard or the third-party BenchLeader tracker (the two sources disagree by roughly 0.3 percentage points, likely due to evaluation-run variance rather than a real capability difference). Grok 4.7 doesn’t have a published SimpleQA score at the time of writing.
Put together, GPT-6 Astra is the most consistent performer on pure factual grounding and recall across both tests that have data for it. That’s a genuinely useful signal if your chatbot use case involves summarizing source documents, answering factual questions, or any workflow where a wrong-but-confident answer is costly — legal research, technical documentation, or medical information lookups, for instance, where you want a model that sticks close to what its sources actually say.
Agentic Task Performance: CursorBench, Terminal-Bench, and DeepSWE
Raw reasoning ability and the ability to actually get work done autonomously are not the same skill, and the agentic benchmarks make that split obvious. CursorBench 4.0, an independent agentic-coding tracker, tested several models at different reasoning-effort settings and found Claude Fable 5.1 (max effort) leading at 51.8% — but at a steep cost of $17.28 per completed task on average, according to figures compiled by Artificial Analysis and independent trackers. GPT-6 Astra wasn’t included in this particular CursorBench run at the time of testing, so its score there is simply unavailable rather than low.
| Model (reasoning effort) | CursorBench 4.0 Score | Avg. Cost per Task |
|---|---|---|
| Claude Fable 5.1 (max) | 51.8% | $17.28 |
| Grok 4.7 (xhigh) | 46.3% | $6.01 |
| Grok 4.7 (high) | 43.9% | $4.69 |
| GPT-5.6 Sol (max) | 41.7% | $8.23 |
| Grok 4.6 (xhigh) | 41.4% | $6.10 |
| Gemini 3.8 Flash (high) | 39.6% | $4.70 |
Notice that Grok 4.7 actually performs respectably here — 46.3% at its highest reasoning setting, ahead of the previous-generation GPT-5.6 Sol and Grok 4.6 — at less than half the per-task cost of Claude Fable 5.1’s top score. This is the clearest evidence that Grok 4.7’s low Intelligence Index composite doesn’t mean it’s uniformly weak; it’s specifically behind on the broader reasoning mix that composite measures, while remaining cost-competitive on focused agentic coding work.
Terminal-Bench 4.0 tells a starker story for Gemini. GPT-6 Astra scores 57.9%, Claude Fable 5.1 scores 55.8%, and Gemini 3.8 Flash scores just 19.1% — a gap wide enough that it can’t be explained by benchmark noise. Part of that is architectural: 3.8 Flash is a lightweight, fast-inference model built for cost efficiency, not a flagship reasoning tier, and Google’s own Gemini 3.8 Pro (which would be the more direct competitor to Astra and Fable 5.1 on agentic depth) doesn’t yet have published Terminal-Bench or Intelligence Index figures. Comparing a budget Flash tier against two full flagship models on an agentic-execution test isn’t entirely apples to apples, and it’s a limitation worth flagging rather than glossing over.
Why Agentic Scores and Reasoning Scores Diverge
The short version: single-turn reasoning tests reward a model for getting one answer right. Agentic benchmarks reward a model for staying coherent across dozens of tool calls, recovering when a command fails, and not losing track of the original goal after dozens of intermediate steps. A model can ace GPQA Diamond and still stumble on Terminal-Bench if its error-recovery behavior is weak, which appears to be exactly what’s happening with Gemini’s Flash tier — strong on FACTS Grounding, dramatically weaker on multi-step agentic execution.
API Pricing Compared: Cost Per Million Tokens
If you’re building on these models rather than just chatting with them, pricing swings are dramatic. GPT-6 Astra and Claude Fable 5.1 are priced identically at the headline level — $10 per million input tokens, $50 per million output tokens — though their cache-discount structures differ: Astra’s cached input runs $1 per million tokens (a 90% discount), while Fable 5.1’s cached reads cost just $0.25 per million, a steeper cut compared to the prior Fable 5 generation. Astra also charges a premium for long-context requests above 272,000 tokens ($20/$75 per million) and offers batch processing at $5/$25.
Gemini 3.8 Flash undercuts both by a wide margin: $0.75 per million input tokens and $3.75 per million output tokens, an introductory rate that Google has said will rise to $1.50/$7.50 on January 1, 2027, per Google’s own AI pricing documentation. That’s roughly one-thirteenth the input cost of GPT-6 Astra or Claude Fable 5.1 — a gap that explains why Flash-tier models dominate high-volume, cost-sensitive applications like customer support bots and content moderation pipelines, even when their raw reasoning scores trail the flagships.
Grok 4.7 sits in the middle: $2 per million input tokens and $6 per million output tokens for requests under 200,000 tokens of context, rising to $4/$12 at or above that threshold. One independent cost-efficiency comparison, reported by Tech Times, found that at an equivalent Intelligence Index score, GPT-6 Astra costs roughly $3.26 per completed task on a weighted-average basis versus $7.63 for Claude Fable 5.1 — meaning Astra can be the cheaper option per unit of measured capability even at an identical headline token price, because it tends to use fewer tokens to reach a comparable answer.
Consumer Subscription Plans Compared
For everyday users who aren’t touching the API, subscription pricing is the number that matters. Here’s how the four providers stack up as of September 2026.
| Provider | Free Tier | Entry Paid Tier | Mid Tier | Top Consumer Tier |
|---|---|---|---|---|
| ChatGPT (OpenAI) | Yes, limited access | Plus — $20/month | Pro — $100/month (5x usage) | Pro — $200/month (20x usage; new sign-ups paused since Sept 10, 2026) |
| Claude (Anthropic), see Anthropic’s pricing page | Yes, daily caps | Pro — $20/month ($17/month billed annually) | Max 5x — $100/month | Max 20x — $200/month |
| Gemini (Google) | Yes, limited access | AI Pro (Gemini Advanced) — $19.99/month | AI Ultra 5x — $99.99/month | AI Ultra 20x — $199.99/month |
| Grok (xAI) | Yes, limited access | SuperGrok — $30/month ($300/year) | SuperGrok Plus — roughly $100/month | SuperGrok Heavy — roughly $300/month (reported introductory discounts to $99/month for new subscribers) |
Two things stand out. First, ChatGPT’s $200 Pro tier has stopped accepting new sign-ups as of September 10, 2026, while existing subscribers keep their access — a supply-management move that suggests OpenAI is managing compute capacity rather than demand. Second, Grok remains the only one of the four charging a premium over the industry-standard $20 entry price, at $30/month for SuperGrok, which is a harder sell given that Grok 4.7 currently trails on the composite Intelligence Index.
Which Tier Actually Matches Your Usage
Most individual users never need anything above the $20 entry tier — the $100 and $200 tiers exist for people running dozens of long agentic sessions per day, not for someone asking a chatbot a few dozen questions. If you’re not hitting rate limits on the entry tier within your first week, you don’t need the upgrade yet.
Business and Enterprise Tiers Compared
Beyond individual subscriptions, all four providers now offer seat-based business plans, and the pricing gaps here are just as wide as on the consumer side. OpenAI’s ChatGPT Business (the renamed ChatGPT Team tier) runs $25 per seat per month billed monthly, or $20 per seat per month billed annually, with a two-seat minimum. Standard Business seats include both ChatGPT and Codex access, running on the gpt-5.6 model family alongside legacy options like gpt-5.4 and o3, rather than defaulting every seat to the pricier Astra tier.
Anthropic’s equivalent Team and Enterprise tiers sit above the consumer Max plans and are sold on contract pricing rather than a fixed public rate, which makes a direct side-by-side comparison harder for procurement teams — you’ll need to talk to Anthropic’s sales team to get an exact seat price rather than reading it off a pricing page. Google bundles its business AI access through Workspace add-ons rather than a standalone consumer-style tier, and xAI has not published a distinct business seat tier as of this writing, leaving SuperGrok’s consumer pricing as the closest available reference point for teams evaluating Grok for internal use.
For a procurement team comparing options at scale, the practical difference is transparency: OpenAI is the only one of the four with a fully public, fixed per-seat business price you can budget against without a sales call. That’s a meaningfully different buying experience than negotiated enterprise contracts, and it’s worth factoring in even before you get to which model scores higher on any given benchmark.
Real-World Use Cases: Where Each Model Wins
Benchmark scores translate into real differences depending on what you’re actually doing. Here’s how the four stack up across common professional scenarios, based on the benchmark categories that map most closely to each task type.
- Long-document legal or compliance review: GPT-6 Astra’s combination of a 922,000-token max input window and an 82.0% FACTS Grounding score makes it the strongest fit for summarizing lengthy contracts or filings without inventing clauses that aren’t in the source text.
- High-volume customer support automation: Gemini 3.8 Flash’s $0.75/$3.75 per-million-token pricing makes it the only realistic option for a support bot fielding tens of thousands of daily conversations, where GPT-6 Astra or Claude Fable 5.1’s pricing would make the workload cost-prohibitive at scale.
- Autonomous coding agents and DevOps automation: Claude Fable 5.1’s 51.8% CursorBench score and 67.4% DeepSWE result make it the top pick for teams running unattended coding agents, despite its higher per-task cost.
- Budget-conscious agentic coding: Grok 4.7’s 46.3% CursorBench score at $6.01 per task undercuts Claude Fable 5.1’s cost by nearly two-thirds while still beating the previous-generation GPT-5.6 Sol, making it a reasonable middle-ground pick for smaller engineering teams.
- Graduate-level research and technical Q&A: GPT-6 Astra’s 96.1% GPQA Diamond score and 75.6% SimpleQA Verified result make it the strongest general-purpose research assistant among the four for questions requiring specialist domain knowledge.
- General writing, brainstorming, and everyday assistant use: With no single benchmark dominating this category, the $20/month entry tiers from ChatGPT, Claude, or Gemini are functionally comparable for most casual users, and the deciding factor is usually interface preference rather than raw capability.
It’s worth noting that these are directional recommendations built from the closest matching benchmark categories, not guarantees of performance on your specific workload. A model’s Terminal-Bench or CursorBench score tells you how it performs on a standardized set of sandboxed tasks, not on your particular codebase, your particular contract template, or your particular support ticket volume. Treat the use-case list as a starting point for a short trial period with each candidate model rather than a substitute for testing against your own data before committing a team to one platform.
One pattern worth flagging: none of the four models wins on both raw capability and price at the same time. GPT-6 Astra and Claude Fable 5.1 trade the top reasoning spot but both charge premium API rates. Gemini 3.8 Flash wins decisively on price but gives up agentic reliability to do it. Grok 4.7 sits in an odd middle position — cheaper than the two flagships, pricier than Gemini, and behind all three on general reasoning breadth while still holding its own on narrower agentic coding tasks. There is no dominant strategy here; every choice is a real tradeoff.
Migration Guide: Switching Chatbots Without Losing Your Workflow
Switching your daily driver between ChatGPT, Claude, Gemini, and Grok is easier on the API side than the consumer-app side, since all four now expose broadly similar chat-completion-style endpoints. If you’re moving a script or integration rather than just your personal habits, the main work is remapping model names and adjusting for context-window differences.
# Illustrative pattern for swapping providers in a single client wrapper
# (endpoint paths and auth headers follow each provider's published API docs)
providers = {
"openai": {"model": "gpt-6-astra", "max_context": 1_050_000},
"anthropic": {"model": "claude-fable-5-1", "max_context": 1_000_000},
"google": {"model": "gemini-3.8-flash", "max_context": 1_048_576},
"xai": {"model": "grok-4.7", "max_context": 500_000},
}
def send_prompt(provider, prompt, history=[]):
cfg = providers[provider]
if estimate_tokens(prompt, history) > cfg["max_context"]:
history = truncate_to_fit(history, cfg["max_context"])
return call_provider_api(provider, cfg["model"], prompt, history)The practical gotcha most people hit first is the context-window mismatch: if you’re moving a workflow built around Astra’s or Fable 5.1’s roughly 1-million-token windows over to Grok 4.7, you’ll need to actively trim conversation history or chunk documents, since Grok’s 500,000-token limit is half the size. Going the other direction — from Grok to Astra, Fable 5.1, or Gemini — doesn’t require any changes, since you’re moving to a larger window.
Exporting and Porting Your Data
For consumer-app users, all four platforms currently support exporting conversation history and custom instructions through their respective account settings pages, though none of them import a competitor’s export format directly. In practice, migrating your actual workflow means re-creating custom instructions or system prompts by hand in the new platform, and re-uploading any reference documents you’d previously attached to a project or workspace.
Pros and Cons of Each Model
GPT-6 Astra’s strengths are breadth and grounding: it leads on GPQA Diamond, FACTS Grounding, and SimpleQA Verified, and its 1,050,000-token context window is among the largest available. Its downside is price — at $10/$50 per million tokens, it’s tied for the most expensive of the four, and OpenAI has already paused new sign-ups for its $200 Pro tier, signaling capacity constraints.
Claude Fable 5.1’s strength is the highest Intelligence Index composite score (65.7) and the strongest agentic-coding result on CursorBench (51.8%), making it the pick for teams building autonomous coding agents. Its downside is cost per task — at $17.28 average per CursorBench task, it’s by far the most expensive option for high-volume agentic workloads.
Gemini 3.8 Flash’s strength is price: at roughly one-thirteenth the token cost of the two flagships, it’s the only viable option for high-volume, low-margin applications. Its downside is agentic depth — a 19.1% Terminal-Bench score is a real weakness for any workflow requiring multi-step autonomous execution, and it’s worth remembering this is Google’s Flash tier, not its flagship Pro tier.
Grok 4.7’s strength is cost-competitive agentic coding — a 46.3% CursorBench score at roughly a third of Claude Fable 5.1’s per-task cost, plus a 71% DeepSWE result that’s close to GPT-6 Astra’s 74.1%. Its downside is general reasoning breadth: a 46-point Intelligence Index score trails the other three measured models by 15 to 20 points, and it’s the only one of the four charging above the standard $20/month entry price for consumer access.
Cost-Efficiency: Intelligence Per Dollar
Sticker price and cost-efficiency aren’t the same thing, and this is where GPT-6 Astra makes its strongest case. Despite an identical headline API price to Claude Fable 5.1, independent tracking from Tech Times found Astra costs about $3.26 per Intelligence Index task on a weighted-average basis versus $7.63 for Fable 5.1 — a 57% cost advantage at comparable measured capability, largely because Astra tends to complete tasks using fewer total tokens. That distinction matters more than the headline per-million-token price for anyone running high volumes of API calls, since actual spend depends on how efficiently a model uses its context, not just what each token costs.
Gemini 3.8 Flash remains the efficiency champion for simpler tasks purely on price, but its weak Terminal-Bench score means that efficiency advantage evaporates fast on any task requiring multiple retries or complex multi-step execution — a failed agentic run that has to be retried three times at $0.75/$3.75 per million tokens can still end up costing more in aggregate than a single successful run on a pricier but more reliable model.
Verdict: Which Chatbot Should You Actually Use
There’s no single winner across ChatGPT vs Claude vs Gemini vs Grok, and the benchmark data makes that explicit rather than obscuring it. If your priority is factual grounding, long-document analysis, or graduate-level research, GPT-6 Astra’s combination of a 96.1% GPQA Diamond score, an 82.0% FACTS Grounding result, and the largest context window makes it the strongest general-purpose pick, provided you can absorb its $10/$50 token pricing or its capped $200/month Pro tier.
If you’re building autonomous coding agents and can tolerate higher per-task cost, Claude Fable 5.1’s leading 65.7 Intelligence Index score and 51.8% CursorBench result justify the premium. If you’re running anything at genuine scale — thousands of daily requests where token cost compounds fast — Gemini 3.8 Flash’s pricing is difficult to argue with, as long as your workload doesn’t require deep multi-step autonomous execution. And if you want agentic coding capability at a moderate price without paying Claude’s premium, Grok 4.7’s CursorBench and DeepSWE numbers make it a legitimate value pick, even though its broader reasoning composite still trails the field by a wide margin.
The honest summary: pick based on the specific benchmark category that maps to your actual workload, not the model with the loudest launch announcement. GPT-6 Astra and Claude Fable 5.1 are trading the top spot depending on which test you look at, Gemini 3.8 Flash wins purely on economics, and Grok 4.7 is still catching up on general reasoning even where it’s competitive on narrower agentic tasks.
Frequently Asked Questions
Which is smarter, ChatGPT or Claude, in September 2026?
It depends on the test. Claude Fable 5.1 leads on the Artificial Analysis Intelligence Index composite (65.7 versus 61.2 for GPT-6 Astra), but GPT-6 Astra leads on GPQA Diamond (96.1%), FACTS Grounding (82.0%), and SimpleQA Verified (75.6%). Neither model wins every category.
Is Grok 4.7 worse than ChatGPT and Claude?
On the general Intelligence Index composite, yes — Grok 4.7 scored 46 versus 61.2 for GPT-6 Astra and 65.7 for Claude Fable 5.1. But on narrower agentic coding benchmarks like CursorBench 4.0 (46.3%) and DeepSWE (71%), Grok 4.7 is competitive with or ahead of previous-generation models like GPT-5.6 Sol, and it costs less per task than Claude Fable 5.1.
Which chatbot has the largest context window?
GPT-6 Astra has the largest at 1,050,000 tokens total (922,000 maximum input), narrowly ahead of Gemini 3.8 Flash’s 1,048,576 tokens and Claude Fable 5.1’s 1,000,000 tokens. Grok 4.7 has the smallest at 500,000 tokens.
Which AI chatbot is cheapest to use via API?
Gemini 3.8 Flash, at an introductory $0.75 per million input tokens and $3.75 per million output tokens, is dramatically cheaper than GPT-6 Astra or Claude Fable 5.1, both priced at $10/$50 per million tokens. That introductory Gemini rate is set to rise to $1.50/$7.50 on January 1, 2027.
Does a higher Intelligence Index score mean better real-world performance?
Not necessarily. The Intelligence Index is a composite of nine different evaluations covering reasoning, coding, and knowledge tasks, but it doesn’t isolate agentic execution reliability. Gemini 3.8 Flash, for example, scores reasonably on FACTS Grounding (73.1%) but posts just 19.1% on Terminal-Bench 4.0, an agentic-execution test — showing that composite reasoning scores and multi-step task reliability can diverge sharply.
Which chatbot subscription is the best value for casual use?
For casual, non-technical use, ChatGPT Plus, Claude Pro, and Google’s AI Pro plan are all priced within a dollar of each other ($19.99–$20/month) and offer comparable everyday functionality. Grok’s SuperGrok plan, at $30/month, is priced higher without a corresponding lead in general benchmark performance.
Why don’t all four models have published GPQA Diamond or Intelligence Index scores?
Benchmark publication cadence varies by lab and isn’t standardized. GPT-6 Astra has a confirmed 96.1% GPQA Diamond score, but Claude Fable 5.1, Gemini 3.8 Flash, and Grok 4.7 hadn’t posted updated GPQA figures on the trackers used for this comparison as of September 23, 2026. Similarly, Gemini 3.8 Flash and Grok 4.7 don’t yet have published scores on the Artificial Analysis Intelligence Index v4.1.1. These gaps reflect incomplete public disclosure, not confirmed weak performance.
Should I switch from ChatGPT to Claude, Gemini, or Grok?
Only if your specific use case maps to a benchmark category where a competitor clearly leads — for example, switching to Gemini 3.8 Flash for high-volume, cost-sensitive automation, or to Claude Fable 5.1 for autonomous coding agents. For general-purpose use, factual research, or long-document work, GPT-6 Astra’s grounding and context-window advantages make switching away from ChatGPT harder to justify on the current data.
