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 Large 3 for open-weight (apache 2.0), self-hostable or strong multilingual performance. On a tight budget at scale, Mistral Large 3 is the value pick.
GLM 5.1 (Z.ai, China) and Mistral Large 3 (Mistral, 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 Large 3 is france's frontier contender — strong multilingual model with European data residency. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Mistral Large 3 is about 2.8× cheaper on input ($0.5/$1.5 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Mistral Large 3 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 4 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 Large 3
Provider
Z.ai (China)
Mistral (France)
Released
April 7, 2026
December 2, 2025
Context window
200K (~300 pages)
256K (~384 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.5/$1.5 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 — A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — A core design strength of GLM 5.1.
Sustained tool use across thousands of calls: GLM 5.1 — A core design strength of GLM 5.1.
Open-weight (Apache 2.0), self-hostable: Mistral Large 3 — A core design strength of Mistral Large 3.
Strong multilingual performance: Mistral Large 3 — A core design strength of Mistral Large 3.
Efficient inference: Mistral Large 3 — A core design strength of Mistral Large 3.
Lowest cost at scale: Mistral Large 3 — At $0.5/$1.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Mistral Large 3 — Its 256K window is about 1.3× larger, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Mistral Large 3 — At $0.5/$1.5 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 Large 3 — 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 open-weight (apache 2.0), self-hostable: Mistral Large 3 — That is its strongest area.
An enterprise with regional data-residency rules: Mistral Large 3 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 Large 3: where it fits
France's frontier contender — strong multilingual model with European data residency. Released December 2, 2025 by Mistral, it is built for open-weight (Apache 2.0), self-hostable, strong multilingual performance, efficient inference, and function calling.
Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $0.5 in / $1.5 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 Large 3 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Large 3 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 Large 3 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 Large 3 leans toward open-weight (apache 2.0), self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.1 or Mistral Large 3?
Mistral Large 3 is cheaper — $1.4/$4.4 per 1M tokens vs $0.5/$1.5 per 1M tokens, roughly 2.8× apart on input.
Which has the bigger context window?
Mistral Large 3 — 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 Large 3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, Mistral Large 3 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 Large 3?
GLM 5.1 — released April 7, 2026, about 4 months after Mistral Large 3.
GLM 5.1 vs Mistral Large 3
Z.ai · China | Mistral · 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 Large 3 for open-weight (apache 2.0), self-hostable or strong multilingual performance. On a tight budget at scale, Mistral Large 3 is the value pick.
GLM 5.1 (Z.ai, China) and Mistral Large 3 (Mistral, 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 Large 3 is france's frontier contender — strong multilingual model with European data residency. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Mistral Large 3 is about 2.8× cheaper on input ($0.5/$1.5 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Mistral Large 3 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 4 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 Large 3
Provider
Z.ai (China)
Mistral (France)
Released
April 7, 2026
December 2, 2025
Context window
200K (~300 pages)
256K (~384 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.5/$1.5 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
A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
A core design strength of GLM 5.1.
Sustained tool use across thousands of calls
GLM 5.1
A core design strength of GLM 5.1.
Open-weight (Apache 2.0), self-hostable
Mistral Large 3
A core design strength of Mistral Large 3.
Strong multilingual performance
Mistral Large 3
A core design strength of Mistral Large 3.
Efficient inference
Mistral Large 3
A core design strength of Mistral Large 3.
Lowest cost at scale
Mistral Large 3
At $0.5/$1.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Mistral Large 3
Its 256K window is about 1.3× larger, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral Large 3
At $0.5/$1.5 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 Large 3
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 open-weight (apache 2.0), self-hostable
→ Mistral Large 3
That is its strongest area.
An enterprise with regional data-residency rules
→ Mistral Large 3 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 Large 3: where it fits
France's frontier contender — strong multilingual model with European data residency. Released December 2, 2025 by Mistral, it is built for open-weight (Apache 2.0), self-hostable, strong multilingual performance, efficient inference, and function calling.
Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $0.5 in / $1.5 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 Large 3 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Large 3 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 Large 3 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.
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 Large 3 leans toward open-weight (apache 2.0), self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.1 or Mistral Large 3?
Mistral Large 3 is cheaper — $1.4/$4.4 per 1M tokens vs $0.5/$1.5 per 1M tokens, roughly 2.8× apart on input.
Which has the bigger context window?
Mistral Large 3 — 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 Large 3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, Mistral Large 3 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 Large 3?
GLM 5.1 — released April 7, 2026, about 4 months after Mistral Large 3.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.