Enter opens the highlighted result. Esc closes.

GLM 4.7 vs Mistral Large

Updated October 21, 2026. 41 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace Mistral Large
GLM 4.7Z.ai
Coding
55.1
Overall
22.2
Output $/M
$2.20
41
Mistral LargeMistral AI
Coding
No data
Overall
5.8
Output $/M
$6.00
0
vs boards won of 41 shared

Decision reading

Only GLM 4.7 has a coding score, 55.1 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 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BMistral LargeMistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
41
GLM 4.7 only
39
Mistral Large only
3
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 4.7 55.1 from 2 benchmarks, Mistral Large not benchmarked.
Widest category
Coding, 44.1 points apart on average over 4 shared benchmarks.
Widest single gap
AIME: GLM 4.7 93.3% against Mistral Large 9.2%, 84.2 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Mistral Large $6.00 per million output tokens, 2.7x apart.
Context window
GLM 4.7 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 4.7BarsMistral LargeTies
Coding40
Intelligence20
Reasoning60
Math40
Knowledge80
General170
All shared boards410
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%37.1%GLM 4.7
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%17.8%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)31.8%Not measuredOnly GLM 4.7 measured
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541275GLM 4.7
LMArena Text: software and IT14531274GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML1447Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
AA Intelligence Index22.25.8GLM 4.7
Epoch Capabilities Index143.6122.1GLM 4.7
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1119Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%Not measuredOnly GLM 4.7 measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
GPQA Diamond80.0%47.7%GLM 4.7
GPQA Diamond (Artificial Analysis run)85.9%35.1%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%3.5%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431257GLM 4.7
LMArena Text: expert14241208GLM 4.7
LMArena Text: hard prompts in English14521268GLM 4.7
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
AIME93.3%9.2%GLM 4.7
MATH 500 (Vals AI)Not measured74.4%Only Mistral Large measured
MGSM (Vals AI)88.2%87.2%GLM 4.7
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241261GLM 4.7
LMArena Text: mathematical work14111257GLM 4.7
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
MMLU Pro82.7%69.7%GLM 4.7
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%63.8%GLM 4.7
MedQA (Vals AI)93.7%76.2%GLM 4.7
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%51.5%GLM 4.7
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
MMLUNot measured68.8%Only Mistral Large measured
LMArena Text: science14571260GLM 4.7
LMArena Text: medicine and healthcare14691255GLM 4.7
LMArena Text: legal and government14441275GLM 4.7
LMArena Text: business and finance14291238GLM 4.7
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
Design Arena: websites1226Not measuredOnly GLM 4.7 measured
Design Arena: UI components1199Not measuredOnly GLM 4.7 measured
Design Arena: game dev1188Not measuredOnly GLM 4.7 measured
Design Arena: data viz1195Not measuredOnly GLM 4.7 measured
Design Arena: 3D1191Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1217Not measuredOnly GLM 4.7 measured
Design Arena: SVG1143Not measuredOnly GLM 4.7 measured
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral LargeLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%Not measuredOnly GLM 4.7 measured
Long context reasoning (AA-LCR)71.0%Not measuredOnly GLM 4.7 measured
DTBenchNot measured60.8%Only Mistral Large measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351265GLM 4.7
LMArena Text: instruction following14121249GLM 4.7
LMArena Text: creative writing14011242GLM 4.7
LMArena Text: English14531284GLM 4.7
LMArena Text: non-English14171235GLM 4.7
LMArena Text: Chinese14951239GLM 4.7
LMArena Text: German14261242GLM 4.7
LMArena Text: Japanese14391170GLM 4.7
LMArena Text: Korean13991202GLM 4.7
LMArena Text: French14321325GLM 4.7
LMArena Text: Spanish14341268GLM 4.7
LMArena Text: Russian14231252GLM 4.7
LMArena Text: Polish14351157GLM 4.7
LMArena Text: multi-turn14461260GLM 4.7
LMArena Text: longer queries14321261GLM 4.7
LMArena Text: writing and language14111261GLM 4.7
LMArena Text: entertainment and media14001230GLM 4.7
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Mistral Large
BenchmarkGLM 4.7Mistral 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 4.7$0.0017Fits in one request
Mistral Large$0.0050Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Mistral Large$0.118Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
Mistral Large$0.460Exceeds the context window

GLM 4.7 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 4.7 and Mistral Large
SpecificationZ.ai GLM 4.7Mistral Large
MakerZ.aiMistral AI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025February 26, 2024
Context window205K128K
Input $/Mtok$0.60$2.00
Output $/Mtok$2.20$6.00
Coding index55.1Not benchmarked
Independent measurements behind it20
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Mistral Large for coding?

Only GLM 4.7 has a coding score, 55.1 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 4.7 or Mistral Large?

GLM 4.7 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 GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Mistral Large?

GLM 4.7, with 205K tokens against 128K.

How many benchmarks is the GLM 4.7 vs Mistral Large comparison based on?

41 benchmarks have a result for both models as of October 21, 2026. GLM 4.7'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.

Compare next