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GPT-5.2-Codex vs Mistral Small 3.2 24B

Mistral Small 3.2 24B costs 46.7x less per output token than GPT-5.2-Codex ($0.30 vs $14.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.

GPT-5.2-CodexOpenAI
Coding
45.4
Overall
28.5
Output $/M
$14.00
Mistral Small 3.2 24BMistral AI
Coding
No data
Overall
No data
Output $/M
$0.30
vs $14.00 vs $0.30 per 1M output tokens. Mistral Small 3.2 24B is 46.7x cheaper.

GPT-5.2-Codex or Mistral Small 3.2 24B: which to pick

Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Mistral Small 3.2 24B has none, so the two cannot be ranked on coding.

Pick Mistral Small 3.2 24B for output tokens 46.7x cheaper ($0.30 vs $14.00 per million).

No benchmark has tested both yet. GPT-5.2-Codex has results on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev; Mistral Small 3.2 24B on Design Arena: websites, Design Arena: UI components and Design Arena: game dev. Compare them on price and context instead.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-5.2-Codex 45.4 from 3 benchmarks, Mistral Small 3.2 24B not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
GPT-5.2-Codex $14.00 against Mistral Small 3.2 24B $0.30 per million output tokens, 46.7x apart.
Context window
GPT-5.2-Codex takes 400K tokens, Mistral Small 3.2 24B 256K.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards measured
Coding benchmarks for GPT-5.2-Codex and Mistral Small 3.2 24B
BenchmarkGPT-5.2-CodexMistral Small 3.2 24BLeader
SWE-bench Verified72.8%Not measuredOnly GPT-5.2-Codex measured
SWE-bench Multilingual66.3%Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev1339Not measuredOnly GPT-5.2-Codex measured
LiveCodeBench (Vals AI)88.0%Not measuredOnly GPT-5.2-Codex measured
SWE-bench Verified (Vals AI)72.4%Not measuredOnly GPT-5.2-Codex measured
Vibe Code Bench (Vals AI)37.9%Not measuredOnly GPT-5.2-Codex measured
Terminal-Bench Hard (Artificial Analysis run)37.1%Not measuredOnly GPT-5.2-Codex measured
Terminal-Bench66.5%Not measuredOnly GPT-5.2-Codex measured
ALE-Bench1299.9Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev: React1329Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev: HTML1348Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 27 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Intelligence benchmarks for GPT-5.2-Codex and Mistral Small 3.2 24B
BenchmarkGPT-5.2-CodexMistral Small 3.2 24BLeader
AA Intelligence Index28.5Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
Agentic work benchmarks for GPT-5.2-Codex and Mistral Small 3.2 24B
BenchmarkGPT-5.2-CodexMistral Small 3.2 24BLeader
Design Arena: agents building web apps1051Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building full-stack apps993Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building mobile apps1090Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building Android apps1176Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building Godot games1142Not measuredOnly GPT-5.2-Codex measured
τ²-Bench Telecom92.1%Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 33 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Reasoning benchmarks for GPT-5.2-Codex and Mistral Small 3.2 24B
BenchmarkGPT-5.2-CodexMistral Small 3.2 24BLeader
GPQA Diamond (Artificial Analysis run)89.9%Not measuredOnly GPT-5.2-Codex measured
Humanity's Last Exam (Artificial Analysis run)35.7%Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 19 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
Web design benchmarks for GPT-5.2-Codex and Mistral Small 3.2 24B
BenchmarkGPT-5.2-CodexMistral Small 3.2 24BLeader
Design Arena: websitesNot measured896Only Mistral Small 3.2 24B measured
Design Arena: UI componentsNot measured907Only Mistral Small 3.2 24B measured
Design Arena: game devNot measured888Only Mistral Small 3.2 24B measured
Design Arena: data vizNot measured929Only Mistral Small 3.2 24B measured
Design Arena: all code categoriesNot measured907Only Mistral Small 3.2 24B measured
Neither model has a result on the other 4 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
General benchmarks for GPT-5.2-Codex and Mistral Small 3.2 24B
BenchmarkGPT-5.2-CodexMistral Small 3.2 24BLeader
IFBench77.6%Not measuredOnly GPT-5.2-Codex measured
Long context reasoning (AA-LCR)82.3%Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 44 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5.2-Codex$0.0088Fits in one request
Mistral Small 3.2 24B$0.0003Fits in one request

Mistral Small 3.2 24B costs less

Repository review

50K input + 3K output tokens

GPT-5.2-Codex$0.130Fits in one request
Mistral Small 3.2 24B$0.0059Fits in one request

Mistral Small 3.2 24B costs less

Agent loop

200K input + 10K output tokens

GPT-5.2-Codex$0.490Fits in one request
Mistral Small 3.2 24B$0.023Fits in one request

Mistral Small 3.2 24B costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5.2-Codex and Mistral Small 3.2 24B
SpecificationOpenAI GPT-5.2-CodexMistral Small 3.2 24B
MakerOpenAIMistral AI
WeightsNo public weights listedOpen weights
ReleasedJanuary 14, 2026June 20, 2025
Context window400K256K
Input $/Mtok$1.75$0.10
Output $/Mtok$14.00$0.30
Coding index45.4Not benchmarked
Independent measurements behind it30
Cost per solved task$0.43Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.2-Codex better than Mistral Small 3.2 24B for coding?

Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Mistral Small 3.2 24B has none, so the two cannot be ranked on coding. Pick Mistral Small 3.2 24B for output tokens 46.7x cheaper ($0.30 vs $14.00 per million).

Which is cheaper, GPT-5.2-Codex or Mistral Small 3.2 24B?

GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Mistral Small 3.2 24B costs $0.10 and $0.30. Cost per solved task is known for GPT-5.2-Codex only.

Which has the bigger context window, GPT-5.2-Codex or Mistral Small 3.2 24B?

GPT-5.2-Codex, with 400K tokens against 256K.

Has any benchmark tested both GPT-5.2-Codex and Mistral Small 3.2 24B?

No benchmark has tested both yet. GPT-5.2-Codex has results on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev; Mistral Small 3.2 24B on Design Arena: websites, Design Arena: UI components and Design Arena: game dev. Compare them on price and context instead. Data as of October 21, 2026.

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