Mistral Small 3.2 24B vs Muse Glimmer

Mistral AI · France  |  Meta · US · Updated June 2026

Quick verdict

Pick Mistral Small 3.2 24B for extremely cheap open-weight model at about $0.075/$0.20 hosted or self-hostable under apache-2.0 with no per-token cost. Pick Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine. On a tight budget at scale, Muse Glimmer is the value pick.

Mistral Small 3.2 24B (Mistral AI, France) and Muse Glimmer (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Mistral Small 3.2 24B is mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. Muse Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecMistral Small 3.2 24BMuse Glimmer
ProviderMistral AI (France) Meta (US)
ReleasedJune 20, 2025 August 10, 2026
Context window256K (~384 pages) 128K (~197 pages)
Price (in/out)$0.075/$0.2 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Extremely cheap open-weight model at about $0.075/$0.20 hosted

Mistral Small 3.2 24B

Muse Glimmer is comparatively weak here — a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships

Self-hostable under Apache-2.0 with no per-token cost

Mistral Small 3.2 24B

Its 256K window holds about 2× more than Muse Glimmer's 128K in a single prompt.

Instruction following and function calling at 24B scale

Mistral Small 3.2 24B

Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality — and it carries the larger 256K context.

Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit

Muse Glimmer

Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.

Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine

Muse Glimmer

Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; Mistral Small 3.2 24B does not.

Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery

Muse Glimmer

Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; Mistral Small 3.2 24B does not.

Lowest cost at scale

Muse Glimmer

Its weights are open, so at volume you pay for your own hardware instead of Mistral Small 3.2 24B's $0.075/$0.2 per 1M tokens.

Largest single-prompt input

Mistral Small 3.2 24B

Its 256K window is about 2× larger than Muse Glimmer's 128K, fitting roughly 384 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Muse Glimmer

At Open weight (self-host / free) it undercuts Mistral Small 3.2 24B, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Mistral Small 3.2 24B

Larger 256K window fits more in one prompt.

Anyone whose priority is extremely cheap open-weight model at about $0.075/$0.20 hosted

Mistral Small 3.2 24B

It is specifically built for that.

Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit

Muse Glimmer

That is its strongest area.

An enterprise with regional data-residency rules

Muse Glimmer or Mistral Small 3.2 24B

Origin (France vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

Mistral Small 3.2 24B: where it fits

Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. Released June 20, 2025 by Mistral AI, it is built for extremely cheap open-weight model at about $0.075/$0.20 hosted, self-hostable under Apache-2.0 with no per-token cost, instruction following and function calling at 24B scale, and runs on modest hardware for local or private deployment.

Its trade-offs are real: a 24B small model — not a frontier reasoner, context reported as 256K but some references cite 128K native, no published SWE-Bench Verified score, and hosted prices vary by provider; the figure shown is a common host rate. At $0.075 in / $0.2 out per million tokens, it sits in the budget price band.

Muse Glimmer: where it fits

Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.

Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Mistral Small 3.2 24B (France) and Muse Glimmer (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Muse Glimmer is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both Mistral Small 3.2 24B and Muse Glimmer without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Mistral Small 3.2 24B or Muse Glimmer better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Mistral Small 3.2 24B leans toward extremely cheap open-weight model at about $0.075/$0.20 hosted while Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Mistral Small 3.2 24B or Muse Glimmer?

Muse Glimmer is cheaper — $0.075/$0.2 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

Mistral Small 3.2 24B — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Mistral Small 3.2 24B and Muse Glimmer together?

Yes — a multi-model platform like LumiChats gives you Mistral Small 3.2 24B, Muse Glimmer and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Mistral Small 3.2 24B or Muse Glimmer?

Muse Glimmer — released August 10, 2026, about 14 months after Mistral Small 3.2 24B.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.