Summarization quality depends on how much context the model can hold without losing important signal. These picks are ranked for their ability to compress real document length into useful, accurate summaries.
Last verified:
/Rankings refresh daily when model data changes
Rankings refresh dailyScored on 6 criteriaNo paid rankings
Best pick right now
OpenAIPremium
GPT-6 Astra
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Your tasks are well-scoped and latency-sensitive — Sonnet 5.5 is half the price and Anthropic says it is the fastest Sonnet yet.
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 — two and a half times GPT-5.6 Sol's $4/$20 promotional rate, five times GPT-6.1 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.
AnthropicPremium
Claude Fable 5
Anthropic's June 9, 2026 Mythos-class flagship. At launch it was the most capable coding model anyone could use — 80.3% SWE-Bench Pro, an 11-point jump over Opus 4.8 — and it has since been overtaken by Claude Fable 5.1 and Claude Opus 5.5. 1M context, 128K output, native parallel subagents.
Verdict
Superseded by Fable 5.1 — still 80.3% SWE-Bench Pro, but 5.1 is better and cheaper to run.
Quality score
97%
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 for coding, agents, and scientific work. On SWE-bench Pro the cheaper Claude Opus 5.5 now scores higher (89.9% vs 81.2% in Anthropic's table). 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
Anthropic's highest-ceiling model — strongest on open-ended research; Opus 5.5 now leads coding.
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 September 22, 2026 Opus — a step up from Opus 5 on agentic coding, long-running agent work and vision, at a lower $4/$20 list price and with fewer tokens spent per finished task.
Verdict
Anthropic's new coding default — top SWE-bench Pro score at a lower price than Opus 5.
Quality score
98%
Pricing
$4.00/1M in
$20.00/1M out
Speed
Deliberate
2/5 speed
Context
1M tokens
Released September 22, 2026. Gateway id anthropic/claude-opus-5.5. $4/$20 per 1M (cache read $0.20, cache write $5); Fast mode $8/$40. 1,000,000 context, 128,000 max output, knowledge cutoff June 2026, effort levels low to max. Launch figures as reported by Anthropic: SWE-bench Pro 89.9 (Fable 5.1 81.2, Opus 5 79.2); SWE-bench Multilingual 93.9; Terminal-Bench 4.0 66.4 (xhigh); OSWorld 2.1 81.8; Humanity's Last Exam 67.7; Toolathlon Verified 77.8 (Opus 5 80.6). Verified October 10, 2026.
CodingAgenticFlagship1M contextPremiumNew
Best for
Agentic coding, long-running agents and knowledge work where Opus 5 was the bar
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
Strong premium coder, now replaced by the cheaper Opus 5.5.
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
The default answer for summarizing — 100/100 on the long-context axis, and the model we would start with unless the price below rules it out.
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 — two and a half times GPT-5.6 Sol's $4/$20 promotional rate, five times GPT-6.1 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, GPT-6.1 Sol at $2/$10 is the value default — OpenAI says it comes close to Astra at a fifth of the price.
Full pricing, benchmark table and release notes on the GPT-6 Astra page.
In this line-up because of context depth: the pick for summarizing when the input is too big to chunk.
Anthropic's June 9, 2026 Mythos-class flagship. At launch it was the most capable coding model anyone could use — 80.3% SWE-Bench Pro, an 11-point jump over Opus 4.8 — and it has since been overtaken by Claude Fable 5.1 and Claude Opus 5.5. 1M context, 128K output, native parallel subagents.
Input
$10.00/1M
Output
$50.00/1M
Context
1M tokens
Speed
Deliberate
What people actually use it for
Autonomous agents that plan, write, run, and debug across an entire codebase with minimal supervision
Whole-repo refactors and PR review where accuracy outranks latency or cost
80.3% SWE-Bench Pro at launch, up from Opus 4.8's 69.2% and GPT-5.5's 58.6%
1932 on GDPval-AA, ahead of Opus 4.8 (1890) and GPT-5.5 (1769)
1M-token context at standard pricing, 128K max output per request
Mythos-class capability released for general use with new cyber-risk safeguards
Where it falls down
Priced at $10/$50 per 1M tokens — double Opus 4.8 ($5/$25)
Deliberate pace; not for latency-sensitive interactive apps
Standard-use safeguards block some high-risk security workloads (use Mythos 5 with partner access)
Skip it if
You are latency- or cost-sensitive, or your tasks don't need frontier-level reasoning — Opus 4.8 at half the price is plenty.
Our verdict
Superseded by Claude Fable 5.1 on September 1, 2026, which beats it on every benchmark Anthropic published and costs less to run thanks to a 75% cache-read cut. Fable 5 remains a frontier-class model — 80.3% SWE-Bench Pro, an 11-point leap over Opus 4.8 — and stays available at the same $10/$50, but there is no reason to start new work on it.
The value option for summarizing: about 60% less per token than GPT-6 Astra, at 98/100 on long-context. Worth starting here and moving up only if the output disappoints.
Input
$4.00/1M
Output
$20.00/1M
Context
1M tokens
Speed
Deliberate
Anthropic's new coding default — top SWE-bench Pro score at a lower price than Opus 5. Full Claude Opus 5.5 review →
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FAQ
What is the best AI for summarizing?
For summarizing, 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 summarizing jobs. Claude Fable 5 is the closest alternative if it doesn't fit your setup.
Why GPT-6 Astra for summarizing?
Because the work it is built for overlaps closely with summarizing: 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, GPT-6.1 Sol at $2/$10 is the value default — OpenAI says it comes close to Astra at a fifth of the price.
What does it cost to use GPT-6 Astra for summarizing?
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. Claude Opus 5.5 is the cheaper route at roughly $80.00 for the same volume, if summarizing is high-volume enough for price to lead the decision.
When is GPT-6 Astra the wrong choice for summarizing?
$10/$50 per 1M — two and a half times GPT-5.6 Sol's $4/$20 promotional rate, five times GPT-6.1 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 summarizing 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 summarizing?
Claude Opus 5.5 at $4/1M input is the budget option here. Anthropic's new coding default — top SWE-bench Pro score at a lower price than Opus 5. Expect a quality step down on the hardest cases — the usual pattern is to route routine summarizing volume to Claude Opus 5.5 and keep GPT-6 Astra for the work where a wrong answer is expensive.