Claude Sonnet 4.6 vs GPT-5
At a Glance
| Compare | Claude Sonnet 4.6Anthropic |  GPT-5OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #24 of 4660.3 score · 3/3 sources · complete | #41 of 4615.0 score · 2/3 sources · provisional · missing LiveBench · full-core range 10.0–43.3 |
| CostLower is better · Published-token output estimate | #31 of 44$0.198 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #31 of 3847.3 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $3.00Anthropic ↗ · Sep 3, 2026 | $0.625Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $15.00Anthropic ↗ · Sep 3, 2026 | $5.00Openrouter ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,000K | 400K |
| Model facts checked | Sep 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
| Benchmark | Claude Sonnet 4.6 | GPT-5 |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 86.50100% of row best · percent · Claude Sonnet 4.6 (High) | 65.6776% of row best · percent · GPT-5 (High) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 60.42100% of row best · percent · Claude Sonnet 4.6 (High) | 9.8616% of row best · percent · GPT-5 (High) |
| LMArena Search Arenasearch-2026-08-24-d25aabda0010 · arena_rating · leader | 1,221.23100% of row best · rating · claude-sonnet-4-6-search; 95% CI [1216.39472071, 1226.06821163]; votes 134905; rank 7 | 1,132.7793% of row best · rating · gpt-5-search; 95% CI [1126.91788409, 1138.61733849]; votes 20781; rank 30 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,458.26100% of row best · rating · claude-sonnet-4-6; 95% CI [1454.67519650, 1461.85096774]; votes 66208; rank 35 | 1,406.0696% of row best · rating · gpt-5-high; 95% CI [1401.61960044, 1410.50585480]; votes 31428; rank 136 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,282.72100% of row best · rating · claude-sonnet-4-6; 95% CI [1276.36460283, 1289.07266013]; votes 25552; rank 27 | 1,209.2294% of row best · rating · gpt-5-high; 95% CI [1202.34954059, 1216.08580885]; votes 34351; rank 76 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 83.63100% of row best · points · Claude Sonnet 4.6 · 18,675 output tokens / case | 77.8293% of row best · points · GPT-5 · 5,896 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 5 benchmark winsOverall lead | 0 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
| Field | Claude Sonnet 4.6 | GPT-5 |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 | Gpt 5 |
| Model | Claude Sonnet 4.6 | GPT-5 |
| Version | Claude Sonnet 4.6 | GPT-5 |
| Lifecycle | active | active |
| Released | 2026-02-17 | 2025-08-07 |
| Knowledge cutoff | 2025-08-01 | 2024-09-30 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 400K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
Claude Sonnet 4.6 Capabilities
GPT-5 Capabilities
Related Comparisons
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|---|---|---|---|
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Claude Sonnet 4.6Anthropic | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Sonnet 4.6 vs GPT-5 FAQs
Is Claude Sonnet 4.6 or GPT-5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.6 and GPT-5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Sonnet 4.6 or GPT-5?+
Claude Sonnet 4.6 is $3.00 and GPT-5 is $0.625 per million tokens, so GPT-5 is cheaper on this metric. Claude Sonnet 4.6 is $15.00 and GPT-5 is $5.00 per million tokens, so GPT-5 is cheaper on this metric.
Which has a larger context window, Claude Sonnet 4.6 or GPT-5?+
Claude Sonnet 4.6 has the larger sourced context window. Claude Sonnet 4.6 supports 1,000K and GPT-5 supports 400K.
Which performs better in benchmarks, Claude Sonnet 4.6 or GPT-5?+
Claude Sonnet 4.6 leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Claude Sonnet 4.6 or GPT-5 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Sonnet 4.6 is not marked open weight; GPT-5 is not marked open weight.
Can Claude Sonnet 4.6 and GPT-5 understand images?+
Claude Sonnet 4.6 is documented with image input; GPT-5 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Sonnet 4.6 or GPT-5?+
Neither has a larger sourced maximum output. Claude Sonnet 4.6 is 128K and GPT-5 is 128K.
Do Claude Sonnet 4.6 and GPT-5 support reasoning and tool use?+
Claude Sonnet 4.6: reasoning, tool calling, and image input. GPT-5: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Sonnet 4.6 or GPT-5?+
Claude Sonnet 4.6 has 2 sourced provider routes; GPT-5 has 2, a tie.
Which offers better value, Claude Sonnet 4.6 or GPT-5?+
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.