Mistral Large 3 2512 vs GPT-5.2

Pricing math

Mistral Large 3 2512 costs $0.50/M input and $1.50/M output from the provider; on OpenKey that's $0.515/M input and $1.545/M output after the flat 3% fee. GPT-5.2 runs $1.75/M input and $14.00/M output from the provider, or $1.8025/M and $14.42/M on OpenKey. On a 10M-input/2M-output workload, Mistral totals $8.00 and GPT-5.2 totals $45.50 — a 5.7x difference (input price ratio 0.29, meaning Mistral's input tokens cost 29% of GPT-5.2's). Most of that gap comes from output pricing: GPT-5.2's completion tokens cost roughly 9x Mistral's. If your workload is output-heavy — long code generation, long-form writing — the gap widens further.

Coding and agent benchmarks

Design Arena scores favor GPT-5.2 across the board: 1219 elo / rank 42 in codecategories vs Mistral's 1191 / rank 56, and 1243 / rank 32 in uicomponent vs Mistral's 1157 / rank 59. GPT-5.2 also has agent-arena scores Mistral lacks entirely — fullstack (1110 elo, rank 21), webapps (1156, rank 19), mobileapps (1173, rank 23), androidnative (1074, rank 22), and godotgamedev (1183, rank 13). Artificial Analysis puts Mistral's coding index at 20.1 and agentic index at 5.5, both notably low. If you're building agents that touch multiple files or tools, GPT-5.2 is the better-supported choice.

Context and modality

GPT-5.2 supports a 400,000-token context window with a 128,000-token max output cap. Mistral Large 3 2512 supports 262,144 tokens context with no stated max output limit. That puts Mistral's context at 0.66x GPT-5.2's — still large enough for most document and codebase work, just smaller than GPT-5.2's ceiling. Both accept text, image, and file input and output text only. Neither model publishes a knowledge cutoff date in this data. For workloads that genuinely need to pack in very large documents or repositories, GPT-5.2's window has more headroom; for everything else, the difference won't matter in practice.

Reasoning and control

GPT-5.2 exposes adjustable reasoning effort — none, low, medium, high, xhigh — with medium as the default, so you can dial up compute for hard problems and dial it down for cheap, fast responses. Mistral Large 3 2512 has no reasoning parameter; it supports standard generation controls (temperature, top_p, seed, tool_choice, structured_outputs, frequency/presence penalty) but no effort tuning. If you need to trade latency and cost against answer quality within a single model, GPT-5.2 gives you that lever. Mistral's tradeoff is fixed: one price, one behavior, no dial. Both models run on OpenKey under one API key, with OpenKey adding a flat 3% fee on top of provider list price.

Questions

How much cheaper is Mistral Large 3 2512 than GPT-5.2?

On a 10M-input/2M-output workload, Mistral costs $8.00 and GPT-5.2 costs $45.50 — Mistral is about 5.7x cheaper. Most of the gap is in output pricing: $1.545/M on OpenKey for Mistral versus $14.42/M for GPT-5.2.

Which model is better for coding agents?

GPT-5.2, based on Design Arena data. It has agent-arena benchmarks in fullstack (1110 elo, rank 21), webapps (1156 elo, rank 19), and mobileapps (1173 elo, rank 23) that Mistral doesn't compete in at all. Mistral's Artificial Analysis agentic index is only 5.5.

Does context window size matter here?

GPT-5.2 supports 400,000 tokens versus Mistral's 262,144 — Mistral's window is about 0.66x the size. For most document work that's still plenty, but GPT-5.2 has more room for very large codebases or multi-document tasks.

Can I use both models with one API key?

Yes. Both Mistral Large 3 2512 and GPT-5.2 are available on OpenKey with a single API key, and OpenKey applies a flat 3% fee on top of each provider's list price — no separate accounts needed.