Claude Sonnet 4.6 vs GPT-5 Pro
At a Glance
| Compare | Claude Sonnet 4.6Anthropic |  GPT-5 ProOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #24 of 4660.3 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| 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 | $7.50Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $15.00Anthropic ↗ · Sep 3, 2026 | $60.00Openrouter ↗ · 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 Pro |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 86.50100% of row best · percent · Claude Sonnet 4.6 (High) | 70.1781% of row best · percent · GPT-5 Pro |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 60.42100% of row best · percent · Claude Sonnet 4.6 (High) | 18.3330% of row best · percent · GPT-5 Pro |
| Overall ResultCounted from the protocol-matched rows above | 2 benchmark winsNo overall winner | 0 benchmark winsNo overall winner |
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 Pro |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 | Gpt 5 |
| Model | Claude Sonnet 4.6 | GPT-5 Pro |
| Version | Claude Sonnet 4.6 | GPT-5 Pro |
| Lifecycle | active | active |
| Released | 2026-02-17 | 2025-10-06 |
| 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 Pro Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Claude Sonnet 4.6Anthropic | vs | GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Claude Sonnet 4.6Anthropic | family variantsimage, text |
Claude Sonnet 4.6Anthropic | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Claude Sonnet 4.6Anthropic | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Claude Sonnet 4.6Anthropic | vs | Grok 4.6xAI | cross-developer peersimage, text |
Claude Sonnet 4.6Anthropic | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Claude Sonnet 4.6Anthropic | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Claude Sonnet 4.6Anthropic | cross-developer peersimage, text |
Claude Sonnet 4.6Anthropic | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Claude Sonnet 4.6Anthropic | vs |  Hy4 previewTencent | cross-developer peerstext |
Claude Sonnet 4.6Anthropic | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
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 Pro FAQs
Is Claude Sonnet 4.6 or GPT-5 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.6 and GPT-5 Pro, 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 Pro?+
Claude Sonnet 4.6 is $3.00 and GPT-5 Pro is $7.50 per million tokens, so Claude Sonnet 4.6 is cheaper on this metric. Claude Sonnet 4.6 is $15.00 and GPT-5 Pro is $60.00 per million tokens, so Claude Sonnet 4.6 is cheaper on this metric.
Which has a larger context window, Claude Sonnet 4.6 or GPT-5 Pro?+
Claude Sonnet 4.6 has the larger sourced context window. Claude Sonnet 4.6 supports 1,000K and GPT-5 Pro supports 400K.
Which performs better in benchmarks, Claude Sonnet 4.6 or GPT-5 Pro?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Claude Sonnet 4.6 or GPT-5 Pro be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Sonnet 4.6 is not marked open weight; GPT-5 Pro is not marked open weight.
Can Claude Sonnet 4.6 and GPT-5 Pro understand images?+
Claude Sonnet 4.6 is documented with image input; GPT-5 Pro 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 Pro?+
GPT-5 Pro has the larger sourced maximum output: Claude Sonnet 4.6 supports 128K and GPT-5 Pro supports 272K output tokens.
Do Claude Sonnet 4.6 and GPT-5 Pro support reasoning and tool use?+
Claude Sonnet 4.6: reasoning, tool calling, and image input. GPT-5 Pro: 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 Pro?+
Claude Sonnet 4.6 has 2 sourced provider routes; GPT-5 Pro has 2, a tie.
Which offers better value, Claude Sonnet 4.6 or GPT-5 Pro?+
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.