Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). 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. On a tight budget at scale, Mistral Small 3.2 24B is the value pick.
GLM 5.1 (Z.ai, China) and Mistral Small 3.2 24B (Mistral AI, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Mistral Small 3.2 24B is about 19× cheaper on input ($0.075/$0.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Mistral Small 3.2 24B holds 1.3× more — 256K (~384 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: GLM 5.1 is the newer model by about 10 months (released April 7, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GLM 5.1
Mistral Small 3.2 24B
Provider
Z.ai (China)
Mistral AI (France)
Released
April 7, 2026
June 20, 2025
Context window
200K (~300 pages)
256K (~384 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.075/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it is the newer of the two.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — Mistral Small 3.2 24B is comparatively weak here — no published SWE-Bench Verified score
Sustained tool use across thousands of calls: GLM 5.1 — GLM 5.1 lists sustained tool use across thousands of calls among its strengths; Mistral Small 3.2 24B does not.
Extremely cheap open-weight model at about $0.075/$0.20 hosted: Mistral Small 3.2 24B — At $0.075/$0.2 per 1M tokens it undercuts GLM 5.1 ($1.4/$4.4 per 1M tokens), and that gap compounds at volume.
Self-hostable under Apache-2.0 with no per-token cost: Mistral Small 3.2 24B — Its 256K window holds about 1.3× more than GLM 5.1's 200K 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 runs cheaper at $0.075/$0.2 per 1M tokens.
Lowest cost at scale: Mistral Small 3.2 24B — At $0.075/$0.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Mistral Small 3.2 24B — Its 256K window is about 1.3× larger than GLM 5.1's 200K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Mistral Small 3.2 24B — At $0.075/$0.2 per 1M tokens it undercuts GLM 5.1, 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 long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — It is specifically built for that.
Anyone whose priority is extremely cheap open-weight model at about $0.075/$0.20 hosted: Mistral Small 3.2 24B — That is its strongest area.
An enterprise with regional data-residency rules: Mistral Small 3.2 24B or GLM 5.1 — Origin (China vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GLM 5.1 (China) and Mistral Small 3.2 24B (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Small 3.2 24B 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.
Frequently asked questions
Is GLM 5.1 or Mistral Small 3.2 24B 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, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) while Mistral Small 3.2 24B leans toward extremely cheap open-weight model at about $0.075/$0.20 hosted, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.1 or Mistral Small 3.2 24B?
Mistral Small 3.2 24B is cheaper — $1.4/$4.4 per 1M tokens vs $0.075/$0.2 per 1M tokens, roughly 19× apart on input.
Which has the bigger context window?
Mistral Small 3.2 24B — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.1 and Mistral Small 3.2 24B together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, Mistral Small 3.2 24B 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, GLM 5.1 or Mistral Small 3.2 24B?
GLM 5.1 — released April 7, 2026, about 10 months after Mistral Small 3.2 24B.
GLM 5.1 vs Mistral Small 3.2 24B
Z.ai · China | Mistral AI · France · Updated June 2026
Quick verdict
Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). 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. On a tight budget at scale, Mistral Small 3.2 24B is the value pick.
GLM 5.1 (Z.ai, China) and Mistral Small 3.2 24B (Mistral AI, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Mistral Small 3.2 24B is about 19× cheaper on input ($0.075/$0.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Mistral Small 3.2 24B holds 1.3× more — 256K (~384 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: GLM 5.1 is the newer model by about 10 months (released April 7, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GLM 5.1
Mistral Small 3.2 24B
Provider
Z.ai (China)
Mistral AI (France)
Released
April 7, 2026
June 20, 2025
Context window
200K (~300 pages)
256K (~384 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.075/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it is the newer of the two.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
Mistral Small 3.2 24B is comparatively weak here — no published SWE-Bench Verified score
Sustained tool use across thousands of calls
GLM 5.1
GLM 5.1 lists sustained tool use across thousands of calls among its strengths; Mistral Small 3.2 24B does not.
Extremely cheap open-weight model at about $0.075/$0.20 hosted
Mistral Small 3.2 24B
At $0.075/$0.2 per 1M tokens it undercuts GLM 5.1 ($1.4/$4.4 per 1M tokens), and that gap compounds at volume.
Self-hostable under Apache-2.0 with no per-token cost
Mistral Small 3.2 24B
Its 256K window holds about 1.3× more than GLM 5.1's 200K 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 runs cheaper at $0.075/$0.2 per 1M tokens.
Lowest cost at scale
Mistral Small 3.2 24B
At $0.075/$0.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Mistral Small 3.2 24B
Its 256K window is about 1.3× larger than GLM 5.1's 200K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral Small 3.2 24B
At $0.075/$0.2 per 1M tokens it undercuts GLM 5.1, 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 long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
It is specifically built for that.
Anyone whose priority is extremely cheap open-weight model at about $0.075/$0.20 hosted
→ Mistral Small 3.2 24B
That is its strongest area.
An enterprise with regional data-residency rules
→ Mistral Small 3.2 24B or GLM 5.1
Origin (China vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GLM 5.1 (China) and Mistral Small 3.2 24B (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Small 3.2 24B 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 GLM 5.1 and Mistral Small 3.2 24B 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.
Is GLM 5.1 or Mistral Small 3.2 24B 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, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) while Mistral Small 3.2 24B leans toward extremely cheap open-weight model at about $0.075/$0.20 hosted, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.1 or Mistral Small 3.2 24B?
Mistral Small 3.2 24B is cheaper — $1.4/$4.4 per 1M tokens vs $0.075/$0.2 per 1M tokens, roughly 19× apart on input.
Which has the bigger context window?
Mistral Small 3.2 24B — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.1 and Mistral Small 3.2 24B together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, Mistral Small 3.2 24B 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, GLM 5.1 or Mistral Small 3.2 24B?
GLM 5.1 — released April 7, 2026, about 10 months after Mistral Small 3.2 24B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.