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GPT-5.3-Codex vs Magistral Small 2506

GPT-5.3-Codex (2026) and Magistral Small 2506 (2026) are agentic coding models from OpenAI and MistralAI. GPT-5.3-Codex ships a not-yet-sourced context window, while Magistral Small 2506 ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

GPT-5.3-Codex is safer overall; choose Magistral Small 2506 when reasoning depth matters.

Specs

Released2026-02-052026-01-15
Context window128K
Parameters
Architecturedecoder onlydecoder only
LicenseProprietary1
Knowledge cutoff--

Pricing and availability

GPT-5.3-CodexMagistral Small 2506
Input price$1.75/1M tokens-
Output price$14/1M tokens-
Providers

Capabilities

GPT-5.3-CodexMagistral Small 2506
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on multimodal input: GPT-5.3-Codex, reasoning mode: Magistral Small 2506, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

Pricing coverage is uneven: GPT-5.3-Codex has $1.75/1M input tokens and Magistral Small 2506 has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose GPT-5.3-Codex when coding workflow support are central to the workload. Choose Magistral Small 2506 when reasoning depth are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Is GPT-5.3-Codex or Magistral Small 2506 open source?

GPT-5.3-Codex is listed under Proprietary. Magistral Small 2506 is listed under 1. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for multimodal input, GPT-5.3-Codex or Magistral Small 2506?

GPT-5.3-Codex has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for reasoning mode, GPT-5.3-Codex or Magistral Small 2506?

Magistral Small 2506 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for function calling, GPT-5.3-Codex or Magistral Small 2506?

GPT-5.3-Codex has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for tool use, GPT-5.3-Codex or Magistral Small 2506?

GPT-5.3-Codex has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-5.3-Codex and Magistral Small 2506?

GPT-5.3-Codex is available on OpenRouter. Magistral Small 2506 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.