Sonnet 5.5 vs GPT-6 Luna vs Gemini 3.8: 20x Gap [2026]

Three new AI models shipped inside a 26-day window this September, and none of them are the flagship everyone expected to write about. Claude Sonnet 5.5 landed on September 28, 2026. GPT-6 Luna arrived alongside its pricier sibling Sol on September 22. Gemini 3.8 Flash beat both of them out the door on September 2. None of these are the top-shelf models labs use for their splashiest demos, but they are the models that will actually run the customer support bot, the internal search tool, and the mobile app feature that has to work at a price that survives contact with a finance team.

That is exactly what makes this comparison useful. Line the three up and a 20x price gap shows up between the most expensive and cheapest of the trio, even though all three ship with roughly the same one-million-token context window. Picking wrong here does not just cost a few dollars on a test call, it compounds across millions of production requests. This piece pulls together verified specs, independently reproduced benchmark tables, and real pricing math so you can figure out which of these three “value tier” models belongs in your stack.

The September 2026 Release Wave, in Order

Google moved first. Gemini 3.8 Flash and its security-hardened sibling Gemini 3.8 Flash Cyber went live on September 2, 2026, according to Google’s own announcement on the Google blog. It landed on top of Gemini 3.7 Flash, which had shipped barely three weeks earlier in mid-August, an unusually tight release cadence even by 2026 standards.

OpenAI answered three weeks later. GPT-6 Sol and GPT-6 Luna both went live on September 22, 2026, as the cost-efficient tiers underneath the GPT-6 Astra flagship that had already shipped earlier in the month. Luna is the cheaper of the pair, undercutting Sol by a wide margin on both input and output pricing, and it replaced the prior GPT-5.6 Luna tier at a lower rate than that model had carried.

Anthropic closed out the month. Claude Sonnet 5.5 shipped on September 28, 2026, just six days after GPT-6 Sol and Luna, and it kept Sonnet 5’s exact pricing structure rather than repricing the tier. Anthropic’s own comparison shows Sonnet 5.5 as a straight swap for Sonnet 5 at the API level, with the gains showing up in speed and task efficiency rather than a new price tag.

The pattern across all three labs is the same: none of them repriced their mid-tier or budget-tier model upward. Every one of these three releases either held pricing flat or cut it compared to the model it replaced, which is unusual after two years in which frontier models routinely got more expensive with each generation.

Why the Budget Tier Matters More Than the Flagships Right Now

September 2026 was an unusually crowded month for AI model launches. Release trackers counted 66 separate AI model and tool releases across the industry during the month, with 16 of them landing in the single week ending September 24, according to ThursdAI’s release log. That followed an already busy August, when BenchLM tracked 41 model releases, the highest monthly count in the prior year. Most of the headlines from that wave went to flagship models: GPT-6 Astra, Claude Opus 5.5, Grok 4.7. But flagships are not what most applications actually run on.

The economics explain why. A flagship model exists to top a leaderboard and justify a lab’s research spend. A budget-tier model like Sonnet 5.5, Luna, or Flash exists to handle the actual volume: the millions of daily API calls from chatbots, classifiers, and internal tools where a few cents per request compounds into a real line item on a cloud bill. When OpenAI framed GPT-6 Luna’s launch, the company’s own comparison leaned on cost-per-completed-task math rather than raw benchmark supremacy, because that is the number that matters to a team running the model millions of times a day rather than testing it once in a demo.

That is also why this comparison focuses on these three specific models instead of the more heavily covered Opus 5.5 versus GPT-6 Astra matchup. The flagship comparison decides who wins a benchmark chart. This comparison decides what your monthly infrastructure bill looks like.

Specs at a Glance: Sonnet 5.5 vs GPT-6 Luna vs Gemini 3.8 Flash

Before getting into benchmarks, here is how the three models stack up on the specs that actually decide whether one fits your architecture.

Spec	Claude Sonnet 5.5	GPT-6 Luna	Gemini 3.8 Flash
Provider	Anthropic	OpenAI	Google DeepMind
Release date	September 28, 2026	September 22, 2026	September 2, 2026
Context window	1,000,000 tokens	1.05 million tokens	1,048,576 tokens
Input price (per 1M tokens)	$2.00	$0.10	$0.75 (intro rate)
Output price (per 1M tokens)	$10.00	$0.50	$3.75 (intro rate)
Cached input price	$0.20	Discounted cached-input rate per OpenAI docs	Not separately listed
Price after January 1, 2027	Unchanged	Not announced	$1.50 in / $7.50 out
Input modalities	Text, image	Text (image support tied to GPT-6 family)	Text, image, audio, video
Predecessor tier	Claude Sonnet 5	GPT-5.6 Luna	Gemini 3.7 Flash
Same-family flagship	Claude Opus 5.5	GPT-6 Astra	Gemini 3.8 Flash Cyber (security variant)
Reported output speed	Faster than Sonnet 5, exact tokens/sec not disclosed	Not disclosed	~302 tokens/second (high-reasoning tier)
Free-tier access	Limited access via Claude Free	Powers parts of the ChatGPT free tier	Included in the Gemini free tier with daily limits

The headline takeaway from this table is that context window is basically a wash. All three sit within rounding distance of one million tokens, so nobody is buying more headroom by paying more here. The entire spread in this comparison comes down to price and task-level performance, not raw capacity.

How Each Model Fits Into Its Lab’s Broader Lineup

None of these three models exists in isolation, and understanding where each sits inside its own family clarifies what it’s actually built to do.

OpenAI’s GPT-6 line now runs three deep: Astra at the top as the flagship reasoning and coding model, Sol as the mid-tier workhorse priced identically to Claude Sonnet 5.5, and Luna at the bottom as the volume-and-cost play. Luna replaced GPT-5.6 Luna at a lower price than its predecessor carried, continuing a pattern where OpenAI’s cheapest tier gets cheaper with each generation rather than holding steady.

Anthropic’s lineup is flatter. Claude Opus 5.5 sits at the top for the hardest reasoning and agentic work, priced at $4 input and $20 output per million tokens, twice what Sonnet 5.5 charges. Sonnet 5.5 is explicitly positioned as Anthropic’s default recommendation for most workloads rather than a stripped-down budget option, which is part of why its Terminal-Bench 4.0 score is competitive with, and in this specific case ahead of, its own flagship.

Google’s Gemini 3.8 generation splits differently again, into a standard Flash model and a security-hardened Flash Cyber variant released the same day. Unlike OpenAI and Anthropic, Google did not simultaneously refresh a flagship Pro-tier model alongside Flash, which is part of why so many of Google’s own benchmark comparisons for Gemini 3.8 Flash are run against the prior generation’s Opus and Sonnet tiers rather than the newest ones from other labs.

Pricing Breakdown: Where the 20x Gap Actually Comes From

Put the three price sheets side by side and the gap is not subtle. Claude Sonnet 5.5 charges $10 per million output tokens. GPT-6 Luna charges $0.50 for the same million tokens. That is a 20x difference on output, and the input side lines up almost exactly the same way: $2.00 versus $0.10, again a 20x spread. Gemini 3.8 Flash sits in between both lines at $0.75 input and $3.75 output during its introductory pricing window, which runs through the end of 2026 before Google’s scheduled increase to $1.50 and $7.50 on January 1, 2027.

Workload	Claude Sonnet 5.5 cost	GPT-6 Luna cost	Gemini 3.8 Flash cost
1M tokens in / 1M tokens out	$12.00	$0.60	$4.50
10M tokens in / 2M tokens out	$40.00	$2.00	$15.00
100M tokens in / 20M tokens out	$400.00	$20.00	$150.00
1B tokens in / 200M tokens out	$4,000.00	$200.00	$1,500.00

Run that out to a real production number and the gap stops being abstract. A support bot processing a billion input tokens and 200 million output tokens a month (a realistic volume for a mid-size company fielding tens of thousands of daily conversations) costs $4,000 on Claude Sonnet 5.5, $1,500 on Gemini 3.8 Flash, and $200 on GPT-6 Luna. That is a $3,800 monthly swing between the priciest and cheapest option for functionally the same job.

What the pure token price does not show is task efficiency. OpenAI’s own reporting on AutomationBench 1.0.6 puts GPT-6 Luna’s per-task cost at $0.037 at maximum reasoning effort, against $0.27 for GPT-6 Sol, $3.00 for Claude Opus 5, and $1.73 for GPT-6 Astra on the same benchmark, according to MarkTechPost’s coverage of the release. OpenAI frames that as Luna costing 93% less per completed task than Claude Opus 5 and 96% less than Claude Fable 5, numbers that account for the fact that a cheaper model sometimes needs more tokens, more retries, or more tool calls to finish the same job. Luna does not need more of any of those, which is why its per-token discount and its per-task discount land in roughly the same neighborhood.

Benchmark Deep Dive: Coding and Agentic Performance

Price only matters if the model can do the job, so here is where the three separate on coding and long-horizon agent work, cross-checked against independent reproductions of each lab’s published numbers.

Benchmark	Claude Sonnet 5.5	GPT-6 Luna	Gemini 3.8 Flash
Terminal-Bench 4.0 (current coding)	70.6%	Not separately reported	19.1%
Terminal-Bench 2.1 (agentic terminal work)	Not separately reported	Not separately reported	89.4% (table leader)
DeepSWE v1.1 (long-horizon software engineering)	Not separately reported	Not separately reported	73.7%
AutomationBench 1.0.6 (max reasoning effort)	Not separately reported	20.7% at $0.037/task	Not separately reported
GDPval-AA v2.1 (professional task quality)	1,844	Not separately reported	Not separately reported

Claude Sonnet 5.5’s Terminal-Bench 4.0 jump is the standout number in this whole comparison. Sonnet 5 scored just 10.3% on that same benchmark, which means Sonnet 5.5 improved nearly 7x on a single generational bump, according to a breakdown published by ComputingForGeeks. That score also edges out Claude Opus 5.5’s 66.4% on the identical test, meaning Anthropic’s mid-tier model now beats its own flagship on at least one current coding benchmark, a detail confirmed independently by Unite.AI’s release coverage.

Gemini 3.8 Flash’s story is consistency rather than a single spike. Google’s own model card, cross-checked by DeepMind’s published evaluation, shows it leading Terminal-Bench 2.1 outright at 89.4%, narrowly ahead of Claude Opus 5’s 89.1% and GPT-5.6 Sol’s 88.8%. On DeepSWE v1.1 it trails Claude Opus 5 by just 0.3 points (73.7% versus 74.0%), while comfortably beating its own predecessor Gemini 3.7 Flash, which scored 65.3% on the same test.

GPT-6 Luna is not built to top coding leaderboards, and OpenAI does not market it that way. Its AutomationBench score of 20.7% trails every other model in its own release comparison table, including GPT-6 Sol (33.2%) and GPT-6 Astra (41.4%). The pitch for Luna is cost per completed task, not raw accuracy, and on that specific metric it still wins by a wide margin because $0.037 per task is a fraction of what the higher-scoring models charge to attempt the same job.

Benchmark Deep Dive: Reasoning, Finance, and Legal Agent Tasks

Coding is not the only workload that matters for a budget-tier model. Google’s model card also reports scores on HLE-Verified (a multidisciplinary expert-reasoning test), the Vals Finance Agent v2 benchmark, and Harvey’s Legal Agent benchmark, all cross-checked by third-party trackers including Vellum.ai and Eesel.ai.

Benchmark	Gemini 3.8 Flash	Gemini 3.7 Flash	Claude Opus 5	Claude Sonnet 5
HLE-Verified	54.9%	53.6%	54.4%	31.0%
Vals Finance Agent v2	61.4%	59.0%	58.6%	53.9%
Harvey’s Legal Agent	10.0%	8.8%	6.7%	5.0%

Gemini 3.8 Flash leads all three of those rows, and by a meaningful margin on HLE-Verified against the older Claude Sonnet 5 (54.9% versus 31.0%). That comparison uses Sonnet 5, not the newer Sonnet 5.5, since Google published its model card before Anthropic’s September 28 release, so there is no independently verified head-to-head between Gemini 3.8 Flash and Sonnet 5.5 on these three specific benchmarks yet. What is verifiable is that Anthropic’s own Artificial Analysis Intelligence Index score for Sonnet 5.5 sits at 56, a full 18 points above Sonnet 5, according to The AI Rankings’ benchmark tracker. That puts Sonnet 5.5’s general composite score close to Gemini 3.8 Flash’s reported Artificial Analysis Index of 59 on its high-reasoning tier, even though the two were measured on different dates against different comparison sets.

The practical read: Gemini 3.8 Flash is the strongest of the three on structured, domain-specific agent tasks like financial analysis and legal document review, based on the benchmarks Google chose to publish. Claude Sonnet 5.5 is the strongest on live coding and terminal work. GPT-6 Luna is not competing on either axis, it is competing on cost per completed unit of work.

Context Window and Multimodal Capabilities

All three models cluster around a one-million-token context window: Claude Sonnet 5.5 at 1,000,000 tokens, GPT-6 Luna at 1.05 million tokens (shared with the Sol tier), and Gemini 3.8 Flash at exactly 1,048,576 tokens, confirmed on OpenAI’s pricing documentation and Google’s Cloud developer docs respectively. For most production use cases, that means none of the three will bottleneck on context length before the other two do.

Multimodal support is where the gap opens up. Gemini 3.8 Flash is documented as accepting text, image, audio, and video input, per Google’s own developer guide. That is the widest input range of the three. Claude Sonnet 5.5 continues Anthropic’s pattern of text and image input without native audio ingestion at the API level. OpenAI has not published a separate modality breakdown for Luna apart from the shared GPT-6 family documentation, so treat Luna’s image-input support as inherited from the family rather than independently confirmed for this specific tier.

If a project needs to process audio or video directly, without a separate transcription step, Gemini 3.8 Flash is the only one of the three that handles it natively. If the workload is pure text or text-plus-images, all three are viable and the decision comes back down to price and benchmark fit.

Consumer Subscription Plans vs API Access

Most people will never touch these models through raw API calls, they will hit them through the consumer chat apps. Here is how the subscription side compares, since Free-tier and Plus-tier access is effectively how ChatGPT, Claude, and Gemini users already interact with the budget-tier models in this comparison.

Plan tier	ChatGPT	Claude	Google Gemini
Free	Limited access to current models	Basic access to current Sonnet tier	Gemini 3.8 Flash with generous daily limits
Entry paid tier	Plus, $20/month	Pro, $20/month ($17/month billed annually)	AI Pro, $19.99/month
Power-user tier (5x usage)	Pro, $100/month	Max, $100/month	AI Ultra, $99.99/month
Top usage tier (20x usage)	Pro, $200/month	Max, $200/month	AI Ultra, $199.99/month

The three consumer pricing ladders are close to identical in structure: a free tier, a $20-ish entry paid tier, then $100 and $200 power-user tiers, confirmed across Google’s own AI plans page and matching third-party trackers. That symmetry is notable given how differently the three labs price their underlying APIs. Consumers effectively pay the same amount regardless of which lab’s budget-tier model is doing the work behind the scenes, while developers calling the API directly see the full 20x spread documented earlier in this piece.

Free-Tier Access and Rate Limits

Anyone evaluating these models without an API key yet will hit them first through free consumer access, and the three labs handle that differently. Claude’s free tier gives basic access to the current Sonnet-class model, which as of late September 2026 means Sonnet 5.5 for at least some portion of free users, though Anthropic reserves the right to route free traffic to a lighter model during high-demand periods. ChatGPT’s free tier offers limited access to current models with usage caps that reset over rolling windows, a structure OpenAI has kept in place across the GPT-5.x and GPT-6 generations. Google’s free Gemini tier is the most generous of the three by explicit design, running Gemini 3.8 Flash itself (not a stripped-down variant) with daily limits the company describes as generous rather than publishing an exact number.

That distinction matters for anyone prototyping before committing to a paid API plan. A developer testing Gemini 3.8 Flash can do meaningful evaluation work on the free tier alone, since the free tier runs the actual model being compared here rather than a scaled-down substitute. Testing Claude Sonnet 5.5 or GPT-6 Luna for free is less predictable, since free-tier routing on both platforms can shift depending on server load and account history.

Real-World Use Case Scenarios

These numbers only mean something once they’re attached to actual workloads. Here are five scenarios where the price and benchmark differences translate into a real decision.

Customer support chatbot at scale

A support desk handling 50,000 conversations a month, averaging 2,000 input tokens and 400 output tokens per conversation, generates roughly 100 million input tokens and 20 million output tokens monthly. On GPT-6 Luna that costs about $20 a month. On Gemini 3.8 Flash it costs about $150. On Claude Sonnet 5.5 it costs about $400. For a support bot answering routine questions where GPT-6 Luna’s coding weakness is irrelevant, the cost gap makes Luna the obvious default, with an escalation path to a stronger model for edge cases.

Coding assistant backend for an internal dev tool

A team building an internal code-review bot that runs against every pull request needs a model that scores well on Terminal-Bench-style tasks. Claude Sonnet 5.5’s 70.6% on Terminal-Bench 4.0 makes it the strongest of the three for this job even at 20x the token cost of Luna, because the cost of a bad automated review (a missed bug, a bad merge) outweighs the token bill in almost every case.

Financial document analysis pipeline

A fintech processing quarterly filings and flagging anomalies benefits from Gemini 3.8 Flash’s 61.4% score on the Vals Finance Agent v2 benchmark, the highest of any model in Google’s comparison table, including Claude Opus 5’s 58.6%. Combined with native document and image ingestion, Flash is positioned as the practical middle option for this kind of structured financial workload.

High-volume content tagging and classification

An e-commerce catalog tagging millions of product listings a month with categories and attributes is a textbook case for the cheapest viable model. This is repetitive, low-ambiguity classification work, and GPT-6 Luna’s per-task cost of $0.037 on AutomationBench-style tasks makes it dramatically cheaper than running the same volume through Sonnet 5.5 or Flash, with no meaningful quality loss on simple labeling tasks.

Mobile app feature with audio input

A mobile app that lets users speak a request and get a response (a voice memo summarizer, for example) needs native audio ingestion without a separate transcription API call. Gemini 3.8 Flash is the only model of the three confirmed to accept audio input directly, per Google’s developer documentation, which removes a pipeline step and a point of failure compared to bolting a transcription service in front of Sonnet 5.5 or Luna.

Internal knowledge base search and document Q&A

A company building a search tool over its own internal documentation (wikis, past support tickets, policy documents) needs a model that can hold a large context window and reason accurately over retrieved passages without hallucinating citations. This is a case where all three models’ near-identical one-million-token context windows matter more than the price gap, since the whole point is loading large chunks of retrieved text into a single call. Given the budget involved in most internal tools (lower request volume than a customer-facing product, but higher tolerance for per-query cost), Gemini 3.8 Flash’s balance of price and its 54.9% HLE-Verified score makes it a reasonable default, with Claude Sonnet 5.5 as the upgrade path if answer quality on complex, multi-document questions becomes the bottleneck.

Migration Guide: Moving Between These Three APIs

Switching a production workload from one of these models to another is not a one-line config change, but it is close. Here is the practical path.

Audit current token volume. Pull 30 days of input and output token counts from your current provider’s usage dashboard before estimating switch costs, since the pricing tables above only matter against real volume.
Map context window assumptions. All three models sit near one million tokens, so a straight port rarely requires trimming prompts, but re-verify any hard-coded token-limit logic in your application layer.
Re-test modality-dependent code paths. If your pipeline sends audio or video, confirm the target model actually accepts it. Gemini 3.8 Flash does natively; Claude Sonnet 5.5 and GPT-6 Luna do not, at least not without a separate transcription step.
Run a benchmark subset against your own data. Public benchmarks like Terminal-Bench and HLE-Verified are directional, not a guarantee for your specific prompts. Sample 100-200 real production requests and compare outputs across all three models before committing.
Rebuild prompt caching separately per provider. Claude’s cache pricing ($0.20 per million cached input tokens) and OpenAI’s cached-input discount are structured differently enough that a caching strategy tuned for one provider will not transfer cleanly to another.
Set up a fallback route, not a hard cutover. Route a small percentage of live traffic to the new model first, watch error rates and output quality, then increase the split gradually rather than switching 100% of traffic on day one.
Recalculate cost at your actual output ratio. Output tokens are priced 5x higher than input tokens on all three models, so a workload that generates long responses (like content drafting) will see a bigger swing between providers than a workload that mostly reads and classifies short inputs.
Watch the Gemini pricing cliff. Gemini 3.8 Flash’s rate doubles on January 1, 2027. Any cost model built today needs a second column for post-January pricing if the workload is expected to run past year-end.
Keep the old integration live behind a feature flag. Don’t delete the previous model’s API client code during migration. If the new model underperforms on a subset of real traffic that your test sample missed, a feature flag lets you roll back a single customer segment or endpoint without a full redeploy.
Document the switch for whoever owns the budget. A 20x price swing on the same workload is the kind of number a finance or engineering-leadership review will ask about later. Recording the before-and-after cost, plus the accuracy tradeoff that justified it, saves a repeat investigation six months down the line.

Pros and Cons of Each Model

Claude Sonnet 5.5

Pros: Leads the trio on current coding benchmarks, with a Terminal-Bench 4.0 score that beats Anthropic’s own Opus 5.5. Pricing held flat from Sonnet 5, so there’s no repricing risk for teams already on that tier. Output generation is reported as over 30% faster than the prior generation at the same price.

Cons: The most expensive of the three by a wide margin, at 20x GPT-6 Luna’s rate. No confirmed audio or video input. Weaker showing on the HLE-Verified and finance-agent style benchmarks where Gemini 3.8 Flash leads (comparisons made against the prior Sonnet 5, since Sonnet 5.5 predates Google’s published model card).

GPT-6 Luna

Pros: Dramatically the cheapest option, at $0.10/$0.50 per million tokens. Lowest verified cost per completed task on AutomationBench among all GPT-6 tiers. Ideal for high-volume, low-ambiguity work like classification and tagging.

Cons: Lowest AutomationBench accuracy score (20.7%) of any model OpenAI compared it against in its own release materials. No independently published coding or reasoning benchmark placing it ahead of either competitor. Modality support beyond text is not separately documented.

Gemini 3.8 Flash

Pros: Widest input modality support (text, image, audio, video) of the three. Leads on HLE-Verified, Vals Finance Agent v2, Harvey’s Legal Agent, and Terminal-Bench 2.1 in Google’s own comparison table. Sits in the middle of the price range, cheaper than Sonnet 5.5 while still beating it on several benchmarks.

Cons: Scheduled price increase to $1.50/$7.50 per million tokens on January 1, 2027, a 2x jump from its introductory rate. Weakest of the three on Terminal-Bench 4.0 specifically (19.1%), despite leading the older Terminal-Bench 2.1 suite. No confirmed head-to-head benchmark against Claude Sonnet 5.5, since the two were evaluated on different dates.

Which Model Fits Which Use Case

Choose Claude Sonnet 5.5 for coding assistants, automated code review, and terminal-based agent work where accuracy on current coding benchmarks matters more than per-token cost.
Choose GPT-6 Luna for high-volume classification, tagging, content moderation triage, and any workload where the task is simple enough that a 20x price cut doesn’t cost meaningful accuracy.
Choose Gemini 3.8 Flash for financial and legal document analysis, and for any application that needs native audio or video ingestion without a separate transcription pipeline.
Choose GPT-6 Luna for prototyping and early-stage products where the cost of experimentation needs to stay near zero before a product finds its usage pattern.
Choose Claude Sonnet 5.5 for internal tools that require long, structured outputs, since Anthropic reports 30% faster generation than Sonnet 5 at unchanged pricing.
Choose Gemini 3.8 Flash for multimodal customer-facing apps, like voice-memo transcription and summarization tools, where the built-in audio support removes an integration step.

The Verdict: What the Data Actually Supports

There is no single winner here, and the data doesn’t support pretending otherwise. Claude Sonnet 5.5 wins on current coding benchmarks by a wide margin, scoring 70.6% on Terminal-Bench 4.0 against Gemini 3.8 Flash’s 19.1%, but it costs 20 times more per output token than GPT-6 Luna to get there. Gemini 3.8 Flash wins on breadth, leading four of the cross-lab benchmarks Google chose to publish and offering the only native audio and video input of the three, while sitting at roughly a third of Sonnet 5.5’s price. GPT-6 Luna wins on pure economics, undercutting both rivals by an order of magnitude on token cost and by a similarly wide margin on cost per completed task.

The practical takeaway for anyone building on top of these three models: stop treating “which model is best” as a single question. Route coding and terminal-agent work to Sonnet 5.5. Route financial analysis, legal review, and anything involving audio or video to Gemini 3.8 Flash. Route high-volume, low-stakes classification and tagging to GPT-6 Luna. Multi-model routing costs a little more engineering time up front, but given a 20x price spread between the cheapest and most expensive model in this comparison, that engineering time pays for itself within the first production month for any workload processing more than a few million tokens.

One more caveat worth stating plainly: several of the benchmark comparisons in this piece straddle release dates. Google published its Gemini 3.8 Flash model card on September 23, five days before Claude Sonnet 5.5 shipped, which means every Gemini-versus-Claude table here is technically Gemini 3.8 Flash against Claude Sonnet 5 and Claude Opus 5, not the newer Sonnet 5.5. Anthropic has not yet published a matching cross-lab table for Sonnet 5.5 against Gemini 3.8 Flash or GPT-6 Luna as of this writing. Treat the Sonnet-5.5-specific numbers (Terminal-Bench 4.0, GDPval-AA, the Artificial Analysis Intelligence Index score) as the most current data point for that model, and treat the three-way Gemini table as the most current apples-to-apples data available for Gemini 3.8 Flash against the previous Claude generation. That’s the honest state of the public benchmark record right now, and it will keep shifting until all three labs publish evaluations run on the same day against the same models.

Frequently Asked Questions

Is GPT-6 Luna the same model as GPT-6 Sol, just cheaper?

No. They are separate tiers within the GPT-6 family released on the same day, September 22, 2026. Sol is priced at $2/$10 per million tokens, identical to Claude Sonnet 5.5, while Luna is priced at $0.10/$0.50. Sol also scores higher on AutomationBench (33.2%) than Luna (20.7%), confirming they are distinct models rather than the same weights at different prices.

Why does Claude Sonnet 5.5 cost the same as Claude Sonnet 5?

Anthropic chose to hold Sonnet 5.5’s pricing at exactly $2 input and $10 output per million tokens, matching Sonnet 5 exactly, rather than introducing a new price point. The company’s own materials frame the upgrade as a speed and efficiency gain (over 30% faster output, up to 30% lower cost per completed task through fewer tool calls) rather than a straight price cut on tokens.

When does Gemini 3.8 Flash’s price increase take effect?

Google has scheduled the increase for January 1, 2027, moving from the current introductory rate of $0.75/$3.75 per million tokens to $1.50/$7.50. Any project planning to run on Gemini 3.8 Flash into 2027 should budget for that doubling in advance.

Which of the three models has the largest context window?

They are effectively tied. GPT-6 Luna and Gemini 3.8 Flash both sit at roughly 1.05 million tokens (1,048,576 for Gemini specifically), while Claude Sonnet 5.5 is documented at 1,000,000 tokens. The difference is small enough that it should not be a deciding factor for most applications.

Can GPT-6 Luna or Claude Sonnet 5.5 process audio input?

Neither has confirmed native audio input support at the API level as of this comparison. Gemini 3.8 Flash is the only one of the three with documented audio and video input support, per Google’s developer documentation. Workloads needing audio on the other two models would need a separate transcription step ahead of the API call.

Is it worth paying 20x more for Claude Sonnet 5.5 over GPT-6 Luna?

It depends entirely on the task. For coding and terminal-agent work, Sonnet 5.5’s 70.6% Terminal-Bench 4.0 score against Luna’s much lower AutomationBench performance suggests the premium is justified when output quality directly affects code shipped to production. For high-volume, low-ambiguity classification work, the accuracy gap matters far less and Luna’s cost advantage dominates the decision.

Do these three models compete for the same consumer subscription tier?

Indirectly, yes. ChatGPT’s free and Plus tiers, Claude’s free and Pro tiers, and Gemini’s free and AI Pro tiers are the consumer-facing wrappers around models like these. All three labs price their entry paid consumer tier around $20 a month, despite the underlying API-level pricing for the models compared here varying by 20x.

Which model should a startup pick if it can only integrate one?

For a single-model strategy, Gemini 3.8 Flash is the most balanced pick based on the data here: it costs less than Claude Sonnet 5.5, outperforms it on several cross-lab benchmarks Google published, and adds multimodal input the other two lack. Startups whose core product is code generation should still lean toward Sonnet 5.5 despite the higher cost, and startups doing pure high-volume text classification should still default to GPT-6 Luna for the cost savings.