Claude Opus 4.6 vs GPT-5.4 Pro
Benchmark Performance
Available Benchmarks
| Benchmark | Claude Opus 4.6 | GPT-5.4 Pro |
|---|---|---|
| ARC-AGI-1verified-v1-b2e2a31b3d53 · verified_score · leader | 94.0099% of row best · percent · Claude Opus 4.6 (120K, High) | 94.50100% of row best · percent · GPT-5.4 Pro (XHigh) |
| ARC-AGI-2verified-v2-326661568d5f · verified_score · leader | 69.1783% of row best · percent · Claude Opus 4.6 (120K, High) | 83.33100% of row best · percent · GPT-5.4 Pro (XHigh) |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 2 benchmark winsOverall lead |
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 Opus 4.6 | GPT-5.4 Pro |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 | Gpt 5 4 |
| Model | Claude Opus 4.6 | GPT-5.4 Pro |
| Version | Claude Opus 4.6 | GPT-5.4 Pro |
| Lifecycle | active | active |
| Released | 2026-02-05 | 2026-03-05 |
| Knowledge cutoff | 2025-05-01 | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000,000 | 1,050,000 |
| 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) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
15 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
Claude Opus 4.6 Capabilities
GPT-5.4 Pro Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.6 vs GPT-5.4 Pro FAQs
Is Claude Opus 4.6 or GPT-5.4 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.6 and GPT-5.4 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.6 or GPT-5.4 Pro?+
Claude Opus 4.6 is $5.00 and GPT-5.4 Pro is $30.00 per million tokens, so Claude Opus 4.6 is cheaper on this metric. Claude Opus 4.6 is $25.00 and GPT-5.4 Pro is $180.00 per million tokens, so Claude Opus 4.6 is cheaper on this metric.
Which has a larger context window, Claude Opus 4.6 or GPT-5.4 Pro?+
GPT-5.4 Pro has the larger sourced context window. Claude Opus 4.6 supports 1,000,000 and GPT-5.4 Pro supports 1,050,000.
Which performs better in benchmarks, Claude Opus 4.6 or GPT-5.4 Pro?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Claude Opus 4.6 or GPT-5.4 Pro be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Opus 4.6 is not marked open weight; GPT-5.4 Pro is not marked open weight.
Can Claude Opus 4.6 and GPT-5.4 Pro understand images?+
Claude Opus 4.6 is documented with image input; GPT-5.4 Pro is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 4.6 or GPT-5.4 Pro?+
Neither has a larger sourced maximum output. Claude Opus 4.6 is 128,000 and GPT-5.4 Pro is 128,000.
Do Claude Opus 4.6 and GPT-5.4 Pro support reasoning and tool use?+
Claude Opus 4.6: reasoning, tool calling, and image input. GPT-5.4 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Opus 4.6 or GPT-5.4 Pro?+
Claude Opus 4.6 has 1 sourced provider route; GPT-5.4 Pro has 2, so GPT-5.4 Pro has broader tracked availability.
Which offers better value, Claude Opus 4.6 or GPT-5.4 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.