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Mistral Small 3.2 24B vs GLM 5.3 Flash

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Mistral Small 3.2 24B is the cheaper of the two; neither can be ranked on quality here.

Mistral Small 3.2 24B does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

mistralai

Mistral Small 3.2 24B

Blended / 1M
$0.133
Context
256K
Released
Jun 20, 2025
Overall score
Not evaluated
tool callingimage input

z-ai

GLM 5.3 Flash

Blended / 1M
$0.237
Context
1.3M
Released
Aug 26, 2026
Overall score
71.6
reasoningtool callingimage inputvideo inputprompt caching

Specs and pricing

MetricMistral Small 3.2 24BGLM 5.3 Flash
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

71.6
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.0161
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$0.133win$0.237
Input price / 1M$0.094win$0.150
Output price / 1M$0.250win$0.500
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.050
Context window256K1.3Mwin
Max output tokens16K944Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Mistral Small 3.2 24B on top, GLM 5.3 Flash below, both out of 100.

Agentic coding
56.8
Coding
79.0
Reasoning
77.6
Mathematics
81.2
Data analysis
76.4
Language
77.3
Instruction following
52.8

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadMistral Small 3.2 24BGLM 5.3 Flash
Support chatbot

1.2K in / 400 out × 200K requests

$42.50/mo$68.80/mo
RAG assistant

8K in / 600 out × 100K requests

$90.00/mo$110.00/mo
Coding agent

40K in / 4K out × 20K requests

$95.00/mo$104.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$112.50/mo$182.50/mo
Bulk classification

500 in / 20 out × 5M requests

$259.37/mo$375.00/mo
Run these two through the cost calculator

Which should you pick?

You need to fit large documents in one call

GLM 5.3 Flash

Wider context window — 1.3M against 256K.

You are cost-constrained

Mistral Small 3.2 24B

Cheaper on blended list price at $0.133 per million tokens.

Mistral Small 3.2 24B vs GLM 5.3 Flash FAQ

Which is better, Mistral Small 3.2 24B or GLM 5.3 Flash?

Mistral Small 3.2 24B is the cheaper of the two; neither can be ranked on quality here. Mistral Small 3.2 24B does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

Is Mistral Small 3.2 24B cheaper than GLM 5.3 Flash?

Mistral Small 3.2 24B is cheaper. On a 3:1 input:output blend, Mistral Small 3.2 24B lists at $0.133 per million tokens and GLM 5.3 Flash at $0.237 — Mistral Small 3.2 24B is 1.8× cheaper. Input and output are priced separately — Mistral Small 3.2 24B charges $0.094 in and $0.250 out, GLM 5.3 Flash charges $0.150 and $0.500 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Does Mistral Small 3.2 24B or GLM 5.3 Flash have a bigger context window?

GLM 5.3 Flash has the larger context window: 256K for Mistral Small 3.2 24B against 1.3M for GLM 5.3 Flash. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.

Do Mistral Small 3.2 24B and GLM 5.3 Flash support prompt caching?

GLM 5.3 Flash publishes a cached-input rate of $0.050 per million tokens against a full input rate of $0.150. The catalogue lists no separate cached rate for Mistral Small 3.2 24B, which means the provider does not price it separately here — not that caching is unavailable.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • ScoresLiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.