GPT-6 Astra wins on coding (100 vs 80) and writing quality. Gemini 3.1 Pro wins on price ($2 vs $10/1M input) and context window (2M vs 1.05M). For most workflows, GPT-6 Astra is the stronger default — openai's frontier answer to fable 5.1 — computer-use and agentic-coding leader at $10/$50.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GPT-6 Astra is the safest overall answer here when you want the strongest default instead of the lowest list price.
Grok 4.5 is the lower-cost option to start with when you still need useful output at scale.
Gemini 3.1 Pro is the better pick when response speed matters more than maximum reasoning depth.
GPT-6 Astra leads on coding with a score of 100 vs 80 for Gemini 3.1 Pro.
Gemini 3.1 Pro has the larger context window: 2M vs 1.05M for GPT-6 Astra.
Gemini 3.1 Pro is cheaper at $2/1M input tokens vs $10/1M for GPT-6 Astra.
GPT-6 Astra is the safer default: it is built for computer and browser use, long-horizon agentic coding, and frontier math and science work, which covers most of what people bring to this comparison.
Gemini 3.1 Pro earns its place when your work is mostly research and deep document analysis, even though it loses the overall count here.
Gemini 3.1 Pro is the more cost-efficient option at $2/1M input — GPT-6 Astra costs 5x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
OpenAI / Premium / Sep 4, 2026
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
The fastest way to see where the recommendation shifts when your priority changes.
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Best for research and deep document analysis — 2M context at the best premium price.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-6 AstraOpenAI | $10.00/1M | $50.00/1M | $200 | 1.1M tokens | Deliberate | 100 | 97 | 100 |
| Gemini 3.1 ProGoogle | $2.00/1M | $12.00/1M | $44 | 2M tokens | Balanced | 80 | 82 | 99 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
The new computer-use and agentic-coding ceiling, and OpenAI's first model priced like a Mythos-class Claude. Astra beats Claude Fable 5.1 on Terminal-Bench 4.0 (57.9% vs 55.8%), Terminal-Bench Science (64.6% vs 52.6%) and FrontierMath Tier 4 (97.6% vs 87.8%), and it is the only model with a credible OSWorld 2.0 result above 70%. It loses to Fable 5.1 on Humanity's Last Exam and on Artificial Analysis's index, costs five times GPT-5.6 Sol, and ships without a SWE-bench number. If your work is computer use, browser agents or math, it is the pick; for everyday coding at scale, Sol at $2/$10 remains the value default.
Released September 3, 2026. API ID gpt-6-astra; rolling out over the coming days to ChatGPT Plus, Pro, Business and Enterprise (usage inside existing allowances; GPT-6 Astra Pro for Pro/Business/Enterprise; Enterprise off by default), the OpenAI API, Microsoft Azure and Amazon Bedrock. Standard API pricing $10/$50 per 1M tokens; Fast mode is up to 2x speed at 2x price; cache reads and writes have separate rates. Model docs list 1,050,000 context, 128,000 max output, knowledge cutoff April 30, 2026, reasoning efforts up to 'max'. Published launch numbers (Astra / GPT-5.6 Sol / Fable 5.1 / Opus 5): OSWorld 2.0 72.6 / 65.7 / — / 70.2; Terminal-Bench 4.0 57.9 / 37.3 / 55.8 / 52.3; Terminal-Bench Science 0.1 64.6 / 22.4 / 52.6 / 30.0; FrontierMath Tier 4 v2 97.6 / 83.0 / 87.8 / 73.2; GPQA Diamond 96.0 / 94.6 / 93.7 / 93.7; Humanity's Last Exam w/ tools 57.2 / — / 65.0 / 63.6; AutomationBench 41.4 / 18.1 / 31.4 / 26.9; DeepSWE v1.1 74.1 / 72.7 / 67.4 / 73.7; ARC-AGI-2 95.0 / 92.5 / 90.0 / 90.4; ARC-AGI-3 99.9 (OpenAI responses-API harness; ARC Prize's stateless runs score far lower) / 7.8 / — / 30.2; ExploitBench 100.0 / 78.5 / — / 70; SRE-Bench 88.0 / 55.9; Artificial Analysis Intelligence Index v4.1.1 61.2 / 60.9 / 65.7 / 63.1. Meets the Critical threshold for cybersecurity under OpenAI's Preparedness Framework; advanced cyber workflows gated behind OpenAI Daybreak. All figures from OpenAI's launch post and model docs, verified September 4, 2026.
Google's flagship with the largest context window of any frontier model at 2M tokens, Deep Think reasoning, and the best price-to-performance among premium models.
Your primary use case is writing quality or agentic coding — Claude wins both.
The best research and long-context model available. Handles entire codebases, legal documents, and large datasets in a single pass — at a lower price than GPT-5.4 or Claude Sonnet 4.6.
The 2M context window is a genuine competitive advantage — no other frontier model gets close for document-heavy workflows.
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GPT-6 Astra wins on more of the categories we score — coding, research, long context — so it is the better default of the two. Gemini 3.1 Pro is the better pick when your work is mostly research and deep document analysis. Neither is universally "better": GPT-6 Astra is aimed at computer and browser use and long-horizon agentic coding, Gemini 3.1 Pro at research and deep document analysis.
Gemini 3.1 Pro is cheaper at $2/1M input and $12/1M output. GPT-6 Astra costs $10/1M input and $50/1M output.
Gemini 3.1 Pro has the larger context window at 2M tokens vs GPT-6 Astra's 1.05M. For large document analysis, Gemini 3.1 Pro is the stronger pick.
GPT-6 Astra is better for coding with a score of 100 vs Gemini 3.1 Pro's 80 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Gemini 3.1 Pro is faster with a balanced speed rating (score: 3) vs GPT-6 Astra's deliberate rating (score: 2). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-6 Astra latency penalty is usually invisible.
$10/$50 per 1M — five times GPT-5.6 Sol's $2/$10 and the same premium as Claude Fable 5.1; Fast mode doubles it again. Trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%) and on the Artificial Analysis Intelligence Index (61.2 vs 65.7). OpenAI published no SWE-bench Verified or SWE-bench Pro figure at launch, so it does not appear on our SWE-bench leaderboard. Avoid it if you are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak. That is the main case for looking at Gemini 3.1 Pro instead.
Slower than Flash for everyday lightweight tasks. Claude Sonnet 4.6 is better for writing quality. Avoid it if your primary use case is writing quality or agentic coding — Claude wins both. Against GPT-6 Astra specifically, the gap shows up most on coding (100 vs 80).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-6 Astra runs $200.00 (at $10/1M in and $50/1M out); Gemini 3.1 Pro runs $44.00 (at $2/1M in and $12/1M out). That is a $156.00/month difference — Gemini 3.1 Pro is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-6 Astra for computer and browser use and long-horizon agentic coding, with Gemini 3.1 Pro handling research and deep document analysis. Routing high-volume, low-stakes calls to Gemini 3.1 Pro at $2/1M and reserving GPT-6 Astra for the hard cases is usually the cheapest arrangement that does not cost you quality.