GPT-5.3-Codex vs Mistral Large 3 675B Instruct
GPT-5.3-Codex (2026) and Mistral Large 3 675B Instruct (2025) compare a coding-specialized model against a standalone API model. GPT-5.3-Codex ships a 400k-token context window, while Mistral Large 3 675B Instruct ships a 128k-token context window. On τ-bench, GPT-5.3-Codex leads by 7.6 pts. On pricing, Mistral Large 3 675B Instruct costs $0.50/1M input tokens versus $1.75/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GPT-5.3-Codex is coding-specialized model, while Mistral Large 3 675B Instruct is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | Mistral Large 3 675B Instruct |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | multimodal apps and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 400k | 128k |
| Cheapest output | $14/1M tokens | $1.50/1M tokens |
| Provider routes | 3 tracked | 5 tracked |
| Shared benchmarks | τ-bench leader | 1 rows |
Decision tradeoffs
- GPT-5.3-Codex holds a shared-benchmark lead on τ-bench, ahead by 7.6 points.
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- Mistral Large 3 675B Instruct has the lower cheapest tracked output price at $1.50/1M tokens.
- Mistral Large 3 675B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large 3 675B Instruct uniquely exposes Multimodal in local model data.
- Local decision data tags Mistral Large 3 675B Instruct for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route/tier: OpenRouter
Mistral Large 3 675B Instruct
$775
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $4,125. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- Mistral Large 3 675B Instruct is $12.50/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Mistral Large 3 675B Instruct adds Multimodal in local capability data.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.3-Codex is $12.50/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2025-12-01 |
| Context window | 400k | 128k |
| Parameters | — | 675B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Mistral License |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Non-commercial only |
| Knowledge cutoff | 2025-08 | 2024-11 |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | Mistral Large 3 675B Instruct |
|---|---|---|
| Input price | $1.75/1M tokens | $0.50/1M tokens |
| Output price | $14/1M tokens | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.3-Codex | Mistral Large 3 675B Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | Yes | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.3-Codex | Mistral Large 3 675B Instruct |
|---|---|---|
| τ-bench | 77.8 | 70.2 |
Deep dive
On shared benchmark coverage, τ-bench has GPT-5.3-Codex at 77.8 and Mistral Large 3 675B Instruct at 70.2, with GPT-5.3-Codex ahead by 7.6 points. The largest visible gap is 7.6 points on τ-bench, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on multimodal input: Mistral Large 3 675B Instruct, reasoning mode: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share vision and structured outputs, 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.
For cost, GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider, while Mistral Large 3 675B Instruct lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large 3 675B Instruct lower by about $4.63 per million blended tokens. Availability is 3 providers versus 5, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support and larger context windows are central to the workload. Choose Mistral Large 3 675B Instruct when vision-heavy evaluation, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, GPT-5.3-Codex or Mistral Large 3 675B Instruct?
GPT-5.3-Codex supports 400k tokens, while Mistral Large 3 675B Instruct supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, GPT-5.3-Codex or Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Mistral Large 3 675B Instruct costs $0.50/1M input and $1.50/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or Mistral Large 3 675B Instruct open source?
GPT-5.3-Codex is listed under Proprietary. Mistral Large 3 675B Instruct is listed under Mistral License. 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 vision, GPT-5.3-Codex or Mistral Large 3 675B Instruct?
Both GPT-5.3-Codex and Mistral Large 3 675B Instruct expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, GPT-5.3-Codex or Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct 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.
Where can I run GPT-5.3-Codex and Mistral Large 3 675B Instruct?
GPT-5.3-Codex is available on OpenRouter, OpenAI API, and Vercel AI Gateway. Mistral Large 3 675B Instruct is available on AWS Bedrock, NVIDIA NIM, Mistral AI Studio, Microsoft Foundry, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-10. Data sourced from public model cards and provider documentation.