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GLM 4.6 vs Mistral Large

Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace Mistral Large
GLM 4.6Z.ai
Coding
8.8
Overall
18.5
Output $/M
$2.20
40
Mistral LargeMistral AI
Coding
No data
Overall
5.8
Output $/M
$6.00
0
vs boards won of 40 shared

Decision reading

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BMistral LargeMistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
40
GLM 4.6 only
26
Mistral Large only
4
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
GLM 4.6 8.8 from 2 benchmarks, Mistral Large not benchmarked.
Widest category
Coding, 38.7 points apart on average over 4 shared benchmarks.
Widest single gap
AIME: GLM 4.6 92.7% against Mistral Large 9.2%, 83.5 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Mistral Large $6.00 per million output tokens, 2.7x apart.
Context window
GLM 4.6 takes 205K tokens, Mistral Large 128K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 4.6BarsMistral LargeTies
Coding40
Intelligence10
Reasoning60
Math40
Knowledge80
General170
All shared boards400
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev1341Not measuredOnly GLM 4.6 measured
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%37.1%GLM 4.6
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%17.8%GLM 4.6
Terminal-Bench Hard (Artificial Analysis run)28.8%Not measuredOnly GLM 4.6 measured
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8Not measuredOnly GLM 4.6 measured
LMArena Text: coding14491275GLM 4.6
LMArena Text: software and IT14551274GLM 4.6
LMArena WebDev: HTML1350Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
AA Intelligence Index18.55.8GLM 4.6
Epoch Capabilities IndexNot measured122.1Only Mistral Large measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Agentic work benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1091Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
GPQA Diamond74.5%47.7%GLM 4.6
GPQA Diamond (Artificial Analysis run)78.0%35.1%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)14.5%3.5%GLM 4.6
LMArena Text: hard prompts14401257GLM 4.6
LMArena Text: expert14321208GLM 4.6
LMArena Text: hard prompts in English14451268GLM 4.6
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
AIME92.7%9.2%GLM 4.6
MATH 500 (Vals AI)Not measured74.4%Only Mistral Large measured
MGSM (Vals AI)89.8%87.2%GLM 4.6
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math14321261GLM 4.6
LMArena Text: mathematical work14471257GLM 4.6
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
MMLU Pro82.2%69.7%GLM 4.6
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%63.8%GLM 4.6
MedQA (Vals AI)92.2%76.2%GLM 4.6
MMLU Pro (Artificial Analysis run)82.9%51.5%GLM 4.6
MMLUNot measured68.8%Only Mistral Large measured
LMArena Text: science14561260GLM 4.6
LMArena Text: medicine and healthcare14551255GLM 4.6
LMArena Text: legal and government14481275GLM 4.6
LMArena Text: business and finance14161238GLM 4.6
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1148Not measuredOnly GLM 4.6 measured
Design Arena: game dev1151Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1129Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1166Not measuredOnly GLM 4.6 measured
Design Arena: SVG1109Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
IFBench43.4%Not measuredOnly GLM 4.6 measured
Long context reasoning (AA-LCR)54.0%Not measuredOnly GLM 4.6 measured
DTBenchNot measured60.8%Only Mistral Large measured
LMArena Text14401265GLM 4.6
LMArena Text: instruction following14101249GLM 4.6
LMArena Text: creative writing14121242GLM 4.6
LMArena Text: English14491284GLM 4.6
LMArena Text: non-English14261235GLM 4.6
LMArena Text: Chinese14991239GLM 4.6
LMArena Text: German14471242GLM 4.6
LMArena Text: Japanese13931170GLM 4.6
LMArena Text: Korean14001202GLM 4.6
LMArena Text: French14591325GLM 4.6
LMArena Text: Spanish14361268GLM 4.6
LMArena Text: Russian14181252GLM 4.6
LMArena Text: Polish14471157GLM 4.6
LMArena Text: multi-turn14271260GLM 4.6
LMArena Text: longer queries14221261GLM 4.6
LMArena Text: writing and language14151261GLM 4.6
LMArena Text: entertainment and media14091230GLM 4.6
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Mistral Large
BenchmarkGLM 4.6Mistral LargeLeader
Neither model has a result on 5 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

GLM 4.6$0.0017Fits in one request
Mistral Large$0.0050Fits in one request

GLM 4.6 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
Mistral Large$0.118Fits in one request

GLM 4.6 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
Mistral Large$0.460Exceeds the context window

GLM 4.6 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 GLM 4.6 and Mistral Large
SpecificationZ.ai GLM 4.6Mistral Large
MakerZ.aiMistral AI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 30, 2025February 26, 2024
Context window205K128K
Input $/Mtok$0.60$2.00
Output $/Mtok$2.20$6.00
Coding index8.8Not benchmarked
Independent measurements behind it20
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Mistral Large for coding?

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 4.6 or Mistral Large?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6 or Mistral Large?

GLM 4.6, with 205K tokens against 128K.

How many benchmarks is the GLM 4.6 vs Mistral Large comparison based on?

40 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.

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