Claude Sonnet 4.5 vs GPT-5.3-Codex
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Claude Sonnet 4.5 | GPT-5.3-Codex |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 | Gpt 5 3 |
| Model | Claude Sonnet 4.5 | GPT-5.3-Codex |
| Version | Claude Sonnet 4.5 | GPT-5.3-Codex |
| Lifecycle | active | active |
| Released | 2025-09-29 | Unknown |
| Knowledge cutoff | 2025-01-01 | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 200,000 | 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) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
14 comparable fields · 7 material differences · Pair passes the primary-source comparison gate
Claude Sonnet 4.5 Capabilities
GPT-5.3-Codex Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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GPT-5.3-CodexOpenAI | vs | cross-developer peersimage, text | |
GPT-5.2OpenAI | vs | GPT-5.3-CodexOpenAI | family variantsimage, text |
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GPT-5.3-CodexOpenAI | vs | GPT-5.4 NanoOpenAI | family variantsimage, text |
Claude Sonnet 4.5Anthropic | vs | Claude Sonnet 5Anthropic | family variantsimage, text |
Claude Sonnet 4.5Anthropic | vs | o3OpenAI | cross-developer peersimage, text |
Claude Sonnet 4.5Anthropic | vs | o3-proOpenAI | cross-developer peersimage, text |
Claude Sonnet 4.5Anthropic | vs | Qwen3 VL FlashQwen | cross-developer peersimage, text |
Gemma 4 31BGoogle DeepMind | vs | GPT-5.3-CodexOpenAI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Sonnet 4.5 vs GPT-5.3-Codex FAQs
Is Claude Sonnet 4.5 or GPT-5.3-Codex better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.5 and GPT-5.3-Codex, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Sonnet 4.5 or GPT-5.3-Codex?+
Claude Sonnet 4.5 is $3.00 and GPT-5.3-Codex is $1.75 per million tokens, so GPT-5.3-Codex is cheaper on this metric. Claude Sonnet 4.5 is $15.00 and GPT-5.3-Codex is $14.00 per million tokens, so GPT-5.3-Codex is cheaper on this metric.
Which has a larger context window, Claude Sonnet 4.5 or GPT-5.3-Codex?+
GPT-5.3-Codex has the larger sourced context window. Claude Sonnet 4.5 supports 200,000 and GPT-5.3-Codex supports 400,000.
Which performs better in benchmarks, Claude Sonnet 4.5 or GPT-5.3-Codex?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Sonnet 4.5 or GPT-5.3-Codex be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Sonnet 4.5 is not marked open weight; GPT-5.3-Codex is not marked open weight.
Can Claude Sonnet 4.5 and GPT-5.3-Codex understand images?+
Claude Sonnet 4.5 is documented with image input; GPT-5.3-Codex is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Sonnet 4.5 or GPT-5.3-Codex?+
GPT-5.3-Codex has the larger sourced maximum output: Claude Sonnet 4.5 supports 64,000 and GPT-5.3-Codex supports 128,000 output tokens.
Do Claude Sonnet 4.5 and GPT-5.3-Codex support reasoning and tool use?+
Claude Sonnet 4.5: reasoning, tool calling, and image input. GPT-5.3-Codex: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Sonnet 4.5 or GPT-5.3-Codex?+
Claude Sonnet 4.5 has 1 sourced provider route; GPT-5.3-Codex has 2, so GPT-5.3-Codex has broader tracked availability.
Which offers better value, Claude Sonnet 4.5 or GPT-5.3-Codex?+
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.