Claude Opus 4.7 vs GPT-5.1

At a Glance

Compare
GPT-5.1OpenAI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#11 of 4679.0 score · 3/3 sources · complete#37 of 4625.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 17.1–50.5
CostLower is better · Published-token output estimate#37 of 44$0.286 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#20 of 3852.7 score · 3/3 sources · completeUnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$5.00Anthropic · Sep 3, 2026$1.25Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokens$25.00Anthropic · Sep 3, 2026$10.00Openai · Sep 3, 2026
Context windowMaximum documented tokens1,000K400K
Model facts checkedSep 3, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
BenchmarkClaude Opus 4.7GPT-5.1
ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader93.50100% of row best · percent · Claude 4.7 (High)72.8378% of row best · percent · GPT-5.1 (Thinking, High)
ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader75.83100% of row best · percent · Claude 4.7 (Max)17.6423% of row best · percent · GPT-5.1 (Thinking, High)
LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · leader1,494.71100% of row best · rating · claude-opus-4-7-high; 95% CI [1488.20710383, 1501.21879014]; votes 21957; rank 71,403.1794% of row best · rating · gpt-5.1; 95% CI [1393.82886201, 1412.50542979]; votes 8244; rank 42
LMArena Search Arenasearch-2026-08-24-d25aabda0010 · arena_rating · leader1,233.25100% of row best · rating · claude-opus-4-7; 95% CI [1227.87374811, 1238.63530622]; votes 91394; rank 41,199.4497% of row best · rating · gpt-5.1-search; 95% CI [1194.20592738, 1204.68165473]; votes 59909; rank 14
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,483.36100% of row best · rating · claude-opus-4-7; 95% CI [1479.49740558, 1487.21455301]; votes 61128; rank 121,422.5796% of row best · rating · gpt-5.1; 95% CI [1418.95842118, 1426.18777801]; votes 42982; rank 100
LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader1,316.07100% of row best · rating · claude-opus-4-7-high; 95% CI [1309.27708749, 1322.85317424]; votes 21092; rank 51,235.0894% of row best · rating · gpt-5.1; 95% CI [1227.35514387, 1242.80648733]; votes 10205; rank 65
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified85.49100% of row best · points · Claude Opus 4.7 · 4,840 output tokens / case75.9889% of row best · points · GPT-5.1 · 3,970 output tokens / case
Overall ResultCounted from the protocol-matched rows above6 benchmark winsOverall lead0 benchmark wins

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Side-by-Side Facts

FieldClaude Opus 4.7GPT-5.1
DeveloperAnthropicOpenAI
FamilyClaude 4Gpt 5 1
ModelClaude Opus 4.7GPT-5.1
VersionClaude Opus 4.7GPT-5.1
Lifecycleactiveactive
Released2026-04-162025-11-13
Knowledge cutoff2026-01-012024-09-30
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,000K400K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessAnthropic (Standard), Deepinfra (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolschat, generation, reasoning, structured_outputs, tools

Claude Opus 4.7 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDanthropic/claude-opus-4-7

GPT-5.1 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5.1

Primary Evidence

Sources and Freshness

Questions

Claude Opus 4.7 vs GPT-5.1 FAQs

Is Claude Opus 4.7 or GPT-5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.7 and GPT-5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Claude Opus 4.7 or GPT-5.1?+

Claude Opus 4.7 is $5.00 and GPT-5.1 is $1.25 per million tokens, so GPT-5.1 is cheaper on this metric. Claude Opus 4.7 is $25.00 and GPT-5.1 is $10.00 per million tokens, so GPT-5.1 is cheaper on this metric.

Which has a larger context window, Claude Opus 4.7 or GPT-5.1?+

Claude Opus 4.7 has the larger sourced context window. Claude Opus 4.7 supports 1,000K and GPT-5.1 supports 400K.

Which performs better in benchmarks, Claude Opus 4.7 or GPT-5.1?+

Claude Opus 4.7 leads the current overall benchmark count. The result uses 6 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.

Can Claude Opus 4.7 or GPT-5.1 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Claude Opus 4.7 is not marked open weight; GPT-5.1 is not marked open weight.

Can Claude Opus 4.7 and GPT-5.1 understand images?+

Claude Opus 4.7 is documented with image input; GPT-5.1 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Claude Opus 4.7 or GPT-5.1?+

Neither has a larger sourced maximum output. Claude Opus 4.7 is 128K and GPT-5.1 is 128K.

Do Claude Opus 4.7 and GPT-5.1 support reasoning and tool use?+

Claude Opus 4.7: reasoning, tool calling, and image input. GPT-5.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Claude Opus 4.7 or GPT-5.1?+

Claude Opus 4.7 has 2 sourced provider routes; GPT-5.1 has 2, a tie.

Which offers better value, Claude Opus 4.7 or GPT-5.1?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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