Standard Compute
Model comparison 2026

GPT-5.3-Codex vs Mistral Large 4

GPT-5.3-Codex
OpenAI · Balanced · Proprietary
$5.42/1M · 400K ctx
Mistral Large 4
Mistral · Balanced · Open weights
$1.10/1M · 1.0M ctx

Community vote

Pick a winner in each dimension. You can change your vote any time.

Output Quality
Correctness and depth of what it produces
Agentic Ability
Tool calls, instruction-following, and multi-step tasks
Speed
Tokens per second and time-to-first-token
Value for money
How much capability you get per dollar
Reliability
Consistent results: fewer refusals, loops and format breaks

Specs and pricing side by side

Spec
GPT-5.3-Codex
Mistral Large 4
Maker
OpenAI
Mistral
Blended price / 1M
$5.42
$1.10
Input / output
$1.75 in · $14 out / 1M tokens
$0.68 in · $2.09 out / 1M tokens
Context window
400K
1.0M
Open weights
No
Yes
Tool use
Yes
Yes
Reasoning
Yes
Yes
Output Quality
6.8
7.6
Agentic Ability
5.1
7.1
Speed
8.0
7.0
Value for money
5.0
5.9
Reliability
8.4
8.8

Pricing and capabilities synced from the OpenRouter catalogue. Scores are editorial (0–10).

Verdict: GPT-5.3-Codex or Mistral Large 4?

Updated 2026-10-13

Choose GPT-5.3-Codex if you want existing Codex setups on an API key that still pin GPT-5.3-Codex. Choose Mistral Large 4 if you want open-weight, EU-provenance agent work that needs long context and image input.

In our editorial scoring, Mistral Large 4 leads in 4 of five dimensions (output quality, agentic ability, value for money and reliability), while GPT-5.3-Codex leads in 1. On price, GPT-5.3-Codex runs about $5.42 per 1M tokens (blended) and is proprietary; Mistral Large 4 is about $1.10 and open-weight.

Where GPT-5.3-Codex falls short
  • Deprecated in Codex with ChatGPT sign-in
  • GPT-6.1 Sol scores far higher for about 20% more per request ($0.067 vs $0.055)
Full GPT-5.3-Codex breakdown →
Where Mistral Large 4 falls short
  • Still a public preview, so behavior and price can change
  • 26.8% in Artificial Analysis's Terminal-Bench 4.0 run, well below the frontier models
Full Mistral Large 4 breakdown →

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