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.
Strengths
OSWorld 2.0 72.6% vs 65.7% for GPT-5.6 Sol, in roughly 47% less time per task — the new computer-use ceiling
Terminal-Bench 4.0 57.9% — ahead of Claude Fable 5.1 (55.8%), Opus 5 (52.3%) and GPT-5.6 Sol (37.3%)
FrontierMath Tier 4 (v2) 97.6% vs Fable 5.1's 87.8%; Terminal-Bench Science 64.6% vs 52.6%; GPQA Diamond 96.0%
1.05M context with 96.3% on OpenAI MRCR 8-needle at 512K–1M (Sol: 73.8%) and 128K max output
OpenAI's lowest misaligned-outcome rates to date: 2.4% on its computer-use safety benchmark vs 22.0% for Sol, 0% scope-creep on impossible cyber tasks vs 48%
Weaknesses
$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
Rolling out over days, not instantly: Enterprise access is off by default, advanced cyber tasks are refused outside OpenAI Daybreak, and the safety layer can pause or stop legitimate agent runs
OpenAI's own system card finds its written reasoning harder to monitor than Sol's
Ranked alternatives
Strong backups depending on your budget, workload, and preferred tradeoffs.
GooglePremium
Gemini 3.1 Pro
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.
Verdict
Best for research and deep document analysis — 2M context at the best premium price.
Quality score
89%
Pricing
$2.00/1M in
$12.00/1M out
Speed
Balanced
3/5 speed
Context
2M tokens
The 2M context window is a genuine competitive advantage — no other frontier model gets close for document-heavy workflows.
Research leader2M contextBest value premiumDeep Think
Best for
Research, deep document analysis, and long-context reasoning at competitive pricing
Anthropic's new Mythos-class flagship and the most capable coding model anyone can use — 80.3% SWE-Bench Pro, an 11-point jump over Opus 4.8. 1M context, 128K output, native parallel subagents. Released June 9, 2026.
Verdict
New global #1 — 80.3% SWE-Bench Pro, the most capable model generally available.
Quality score
98%
Pricing
$10.00/1M in
$50.00/1M out
Speed
Deliberate
2/5 speed
Context
1M tokens
Launched June 9, 2026 as the public, Mythos-class release. Available on the Claude API, Microsoft Foundry, and Google Vertex AI. Free for all users until June 22, 2026. Same underlying model as Claude Mythos 5, with safeguards that block specific high-risk cyber responses.
Coding leaderSWE-Bench Pro #1Mythos-classParallel subagentsAgenticLong contextPremiumNew
Best for
The hardest coding tasks, autonomous multi-step agents, and frontier-grade reasoning
Anthropic's September 1, 2026 frontier release and the new capability ceiling for coding, agents, and scientific work. Base pricing is unchanged at $10/$50, but cache reads dropped 75% to $0.25/1M — roughly 25% cheaper on typical workloads and up to 45% cheaper on agentic ones. 1M context, 128K output, adaptive thinking always on.
Verdict
New frontier leader — better than Fable 5 on every published benchmark, and cheaper to run.
Quality score
98%
Pricing
$10.00/1M in
$50.00/1M out
Speed
Deliberate
2/5 speed
Context
1M tokens
Released September 1, 2026 alongside Claude Mythos 5.1, the first update to the Mythos-class line since Fable 5 on June 9. API ID claude-fable-5-1; generally available on the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry. Published launch numbers (Fable 5.1 / Fable 5 / Opus 5 / GPT-5.6 Sol): Terminal-Bench-Science 0.1 52.6 / 24.7 / 29.0 / 22.4; Terminal-Bench 4.0 55.8 / 42.0 / 52.3 / 37.3; CursorBench 3.2.0 73.4 / 70.5 / 70.0 / 67.2; AutomationBench 31.4 / 17.1 / 26.9 / 19.6; OSWorld 2.0 strict 41.7 / 36.1 / 39.6; Humanity's Last Exam (no tools) 60.9 / 57.8 / 56.6; GDPval-AA v2 1853 / 1723 / 1824 / 1711. GDPval-AA v2 is rescaled from the v1 numbers quoted on the Fable 5 page and is not directly comparable to them.
Anthropic's flagship-tier Opus that comes close to Claude Fable 5's frontier intelligence at half the price — the new default for complex agentic coding and enterprise agents.
Verdict
Best premium model for agentic coding — near-Fable 5 quality at half the price.
Quality score
97%
Pricing
$5.00/1M in
$25.00/1M out
Speed
Deliberate
2/5 speed
Context
1M tokens
Released July 24, 2026 at Opus 4.8's exact pricing. 1M context at standard rates, 128K max output. Anthropic's alignment audit calls it their most aligned model to date. Default model on Claude Max plans.
CodingAgenticFlagship1M contextPremium
Best for
Complex agentic coding and enterprise agent workflows
Ranked first here for long context AI: 100/100 on long-context, with the widest margin of anything in this line-up.
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.
Input
$10.00/1M
Output
$50.00/1M
Context
1.1M tokens
Speed
Deliberate
What people actually use it for
Computer-use agents that fill forms, update CRMs, run QA in a browser — 72.6% on OSWorld 2.0 at ~40 minutes per task vs Sol's 75
Agentic coding in Codex with cross-context notes — 57.9% Terminal-Bench 4.0, ahead of Claude Fable 5.1 (55.8%)
Frontier math and science — 97.6% FrontierMath Tier 4 and 64.6% Terminal-Bench Science, both well clear of every rival OpenAI tested
Where it wins
OSWorld 2.0 72.6% vs 65.7% for GPT-5.6 Sol, in roughly 47% less time per task — the new computer-use ceiling
Terminal-Bench 4.0 57.9% — ahead of Claude Fable 5.1 (55.8%), Opus 5 (52.3%) and GPT-5.6 Sol (37.3%)
FrontierMath Tier 4 (v2) 97.6% vs Fable 5.1's 87.8%; Terminal-Bench Science 64.6% vs 52.6%; GPQA Diamond 96.0%
1.05M context with 96.3% on OpenAI MRCR 8-needle at 512K–1M (Sol: 73.8%) and 128K max output
OpenAI's lowest misaligned-outcome rates to date: 2.4% on its computer-use safety benchmark vs 22.0% for Sol, 0% scope-creep on impossible cyber tasks vs 48%
Where it falls down
$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
Rolling out over days, not instantly: Enterprise access is off by default, advanced cyber tasks are refused outside OpenAI Daybreak, and the safety layer can pause or stop legitimate agent runs
OpenAI's own system card finds its written reasoning harder to monitor than Sol's
Skip 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.
Our verdict
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.
Full pricing, benchmark table and release notes on the GPT-6 Astra page.
The value option for long context AI: about 77% less per token than GPT-6 Astra, at 99/100 on long-context. Worth starting here and moving up only if the output disappoints.
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.
Input
$2.00/1M
Output
$12.00/1M
Context
2M tokens
Speed
Balanced
What people actually use it for
Analyzing entire contracts, codebases, or research corpora in a single 2M-token prompt
Due diligence synthesis across large sets of financial documents or legal agreements
Multi-step reasoning across dense technical specifications with Deep Think mode
Where it wins
2M token context window — the largest of any frontier model
Leads ARC-AGI-2 reasoning benchmark at 77.1%
Best price-to-performance among premium models at $2/$12 per 1M tokens
Where it falls down
Slower than Flash for everyday lightweight tasks
Claude Sonnet 4.6 is better for writing quality
Skip it if
Your primary use case is writing quality or agentic coding — Claude wins both.
Our verdict
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.
Also worth a look for long context AI, at 99/100 on the long-context axis.
Input
$10.00/1M
Output
$50.00/1M
Context
1M tokens
Speed
Deliberate
New frontier leader — better than Fable 5 on every published benchmark, and cheaper to run. Full Claude Fable 5.1 review →
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FAQ
What is the best AI for long context AI?
For long context AI, GPT-6 Astra (OpenAI) is our pick. OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50. It costs $10/1M input and $50/1M output tokens, with a 1.05M-token context window — enough headroom for all but the largest long context AI jobs. Gemini 3.1 Pro is the closest alternative if it doesn't fit your setup.
Why GPT-6 Astra for long context AI?
Because the work it is built for overlaps closely with long context AI: computer-use agents that fill forms, update CRMs, run QA in a browser — 72.6% on OSWorld 2.0 at ~40 minutes per task vs Sol's 75 and agentic coding in Codex with cross-context notes — 57.9% Terminal-Bench 4.0, ahead of Claude Fable 5.1 (55.8%). 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.
What does it cost to use GPT-6 Astra for long context AI?
On a moderate month — 10M input and 2M output tokens — GPT-6 Astra runs about $200.00 at list price, with no batch or caching discounts applied, so treat that as a ceiling. Gemini 3.1 Pro is the cheaper route at roughly $44.00 for the same volume, if long context AI is high-volume enough for price to lead the decision.
When is GPT-6 Astra the wrong choice for long context AI?
$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). 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. None of that rules it out for long context AI on its own — but if one of those limits maps onto how you actually work, take the alternative on this page seriously rather than defaulting to the top pick.
Is there a cheaper AI that still handles long context AI?
Gemini 3.1 Pro at $2/1M input is the budget option here. Best for research and deep document analysis — 2M context at the best premium price. Expect a quality step down on the hardest cases — the usual pattern is to route routine long context AI volume to Gemini 3.1 Pro and keep GPT-6 Astra for the work where a wrong answer is expensive.
Which of these is fastest?
Gemini 3.1 Pro, rated balanced against GPT-6 Astra's deliberate. Speed matters most for interactive and high-volume work; if your long context AI runs in the background, the slower and more capable model is usually the better trade.