Claude Sonnet 4.6 vs GPT-5.2 Pro
Benchmark Performance
Available Benchmarks
| Benchmark | Claude Sonnet 4.6 | GPT-5.2 Pro |
|---|---|---|
| ARC-AGI-1verified-v1-15fb467fd4fc · verified_score · leader | 86.5096% of row best · percent · Claude Sonnet 4.6 (High) | 90.50100% of row best · percent · GPT-5.2 Pro (XHigh) |
| ARC-AGI-2verified-v2-9a3db289984e · verified_score · leader | 60.42100% of row best · percent · Claude Sonnet 4.6 (High) | 54.1690% of row best · percent · GPT-5.2 Pro (High) |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winTied overall | 1 benchmark winTied overall |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Claude Sonnet 4.6 | GPT-5.2 Pro |
|---|---|---|
| Developer | Anthropic | Openai |
| Family | Claude 4 | Gpt 5 2 |
| Model | Claude Sonnet 4.6 | GPT-5.2 Pro |
| Version | Claude Sonnet 4.6 | GPT-5.2 Pro |
| Lifecycle | active | active |
| Released | 2026-02-17 | 2025-12-11 |
| Knowledge cutoff | 2025-08-01 | 2025-08-31 |
| Input modalities | Unknown | Unknown |
| Output modalities | Unknown | Unknown |
| Context window | 1,000,000 | 400,000 |
| 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 |
13 comparable fields · 8 material differences · Interactive comparison only; indexing gate not met
Claude Sonnet 4.6 Capabilities
GPT-5.2 Pro Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Sonnet 4.6 vs GPT-5.2 Pro FAQs
Is Claude Sonnet 4.6 or GPT-5.2 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.6 and GPT-5.2 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-5.2 Pro?+
Claude Sonnet 4.6 is $3.00 and GPT-5.2 Pro is $21.00 per million tokens, so Claude Sonnet 4.6 is cheaper on this metric. Claude Sonnet 4.6 is $15.00 and GPT-5.2 Pro is $168.00 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-5.2 Pro?+
Claude Sonnet 4.6 has the larger sourced context window. Claude Sonnet 4.6 supports 1,000,000 and GPT-5.2 Pro supports 400,000.
Which performs better in benchmarks, Claude Sonnet 4.6 or GPT-5.2 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-5.2 Pro be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Sonnet 4.6 is not marked open weight; GPT-5.2 Pro is not marked open weight.
Can Claude Sonnet 4.6 and GPT-5.2 Pro understand images?+
Claude Sonnet 4.6 is not documented with image input; GPT-5.2 Pro is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Sonnet 4.6 or GPT-5.2 Pro?+
Neither has a larger sourced maximum output. Claude Sonnet 4.6 is 128,000 and GPT-5.2 Pro is 128,000.
Do Claude Sonnet 4.6 and GPT-5.2 Pro support reasoning and tool use?+
Claude Sonnet 4.6: reasoning and tool calling. GPT-5.2 Pro: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Sonnet 4.6 or GPT-5.2 Pro?+
Claude Sonnet 4.6 has 2 sourced provider routes; GPT-5.2 Pro has 2, a tie.
Which offers better value, Claude Sonnet 4.6 or GPT-5.2 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.