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GLM 4.7 vs Mistral Medium 3.1

GLM 4.7 leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 22.2 to 9.2, at about the same output price ($2.20 vs $2.00 per million). Updated October 21, 2026; 57 benchmarks have a result for both.

Replace GLM 4.7 Replace Mistral Medium 3.1
GLM 4.7Z.ai
Coding
55.1
Overall
22.2
Output $/M
$2.20
51
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
9.2
Output $/M
$2.00
6
vs boards won of 57 shared

GLM 4.7 or Mistral Medium 3.1: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding.

Pick Mistral Medium 3.1 for output tokens 1.1x cheaper ($2.00 vs $2.20 per million) and work measured by MGSM (Vals AI) or LMArena Text: mathematical work, where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
57
GLM 4.7 only
23
Mistral Medium 3.1 only
17
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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 Medium 3.1 not benchmarked.
Widest category
Agentic work, 55.3 points apart on average over 1 shared benchmark.
Widest single gap
AA Math Index: GLM 4.7 95.0 against Mistral Medium 3.1 38.3, 56.7 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Mistral Medium 3.1 $2.00 per million output tokens, 1.1x apart.
Context window
GLM 4.7 takes 205K tokens, Mistral Medium 3.1 131K.

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 Medium 3.1Ties
Coding70
Intelligence10
Agentic work10
Reasoning50
Math42
Knowledge82
Web design80
General172
All shared boards516
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
Coding benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.320.5GLM 4.7
LiveCodeBench (Vals AI)82.2%44.8%GLM 4.7
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%40.6%GLM 4.7
SciCodeNot measured32.4%Only Mistral Medium 3.1 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%10.6%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5210.2GLM 4.7
LMArena Text: coding14541433GLM 4.7
LMArena Text: software and IT14531436GLM 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 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
AA Intelligence Index22.29.2GLM 4.7
Epoch Capabilities Index143.6Not measuredOnly GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
AA Agentic IndexNot measured1.9Only Mistral Medium 3.1 measured
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 apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%40.6%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
MMMU Pro (Vals AI)Not measured63.0%Only Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)85.9%58.8%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%4.7%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 prompts14431426GLM 4.7
LMArena Text: expert14241408GLM 4.7
LMArena Text: hard prompts in English14521435GLM 4.7
LMArena Vision: diagramsNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: homeworkNot measured1165Only Mistral Medium 3.1 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index42
Math benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
AIME93.3%42.3%GLM 4.7
MATH 500 (Vals AI)Not measured87.0%Only Mistral Medium 3.1 measured
MGSM (Vals AI)88.2%90.9%Mistral Medium 3.1
AA Math Index95.038.3GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%38.3%GLM 4.7
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241408GLM 4.7
LMArena Text: mathematical work14111412Mistral Medium 3.1
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index82
Knowledge benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
MMLU Pro82.7%75.3%GLM 4.7
LegalBench (Vals AI)83.4%62.0%GLM 4.7
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%50.7%Mistral Medium 3.1
TaxEval (Vals AI)68.8%70.3%Mistral Medium 3.1
MortgageTax (Vals AI)Not measured36.5%Only Mistral Medium 3.1 measured
MedQA (Vals AI)93.7%78.2%GLM 4.7
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%68.3%GLM 4.7
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571444GLM 4.7
LMArena Text: medicine and healthcare14691449GLM 4.7
LMArena Text: legal and government14441439GLM 4.7
LMArena Text: business and finance14291418GLM 4.7
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
Design Arena: websites12261133GLM 4.7
Design Arena: UI components11991100GLM 4.7
Design Arena: game dev11891074GLM 4.7
Design Arena: data viz11951144GLM 4.7
Design Arena: 3D11911081GLM 4.7
Design Arena: all code categories12171120GLM 4.7
Design Arena: SVG1143993GLM 4.7
Design Arena: ASCII art1165997GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
General benchmarks for GLM 4.7 and Mistral Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%39.8%GLM 4.7
Long context reasoning (AA-LCR)71.0%42.7%GLM 4.7
DTBenchNot measured67.5%Only Mistral Medium 3.1 measured
LMCANot measured19.1%Only Mistral Medium 3.1 measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351424GLM 4.7
LMArena Text: instruction following14121398GLM 4.7
LMArena Text: creative writing14011391GLM 4.7
LMArena VisionNot measured1172Only Mistral Medium 3.1 measured
LMArena Text: English14531438GLM 4.7
LMArena Text: non-English14171408GLM 4.7
LMArena Text: Chinese14951447GLM 4.7
LMArena Text: German14261432Mistral Medium 3.1
LMArena Text: Japanese14391379GLM 4.7
LMArena Text: Korean13991380GLM 4.7
LMArena Text: French14321460Mistral Medium 3.1
LMArena Text: Spanish14341433GLM 4.7
LMArena Text: Russian14231411GLM 4.7
LMArena Text: Polish14351430GLM 4.7
LMArena Text: multi-turn14461418GLM 4.7
LMArena Text: longer queries14321406GLM 4.7
LMArena Text: writing and language14111395GLM 4.7
LMArena Text: entertainment and media14001390GLM 4.7
LMArena Vision: EnglishNot measured1179Only Mistral Medium 3.1 measured
LMArena Vision: ChineseNot measured1190Only Mistral Medium 3.1 measured
LMArena Vision: reading textNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: humorNot measured1168Only Mistral Medium 3.1 measured
LMArena Vision: captioningNot measured1145Only Mistral Medium 3.1 measured
LMArena Vision: recognizing thingsNot measured1147Only Mistral Medium 3.1 measured
LMArena Vision: creative writingNot measured1200Only Mistral Medium 3.1 measured
Neither model has a result on the other 14 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 Medium 3.1
BenchmarkGLM 4.7Mistral Medium 3.1Leader
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 Medium 3.1$0.0014Fits in one request

Mistral Medium 3.1 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Mistral Medium 3.1$0.026Fits in one request

Mistral Medium 3.1 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
Mistral Medium 3.1$0.100Exceeds the context window

Mistral Medium 3.1 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 Medium 3.1
SpecificationZ.ai GLM 4.7Mistral Medium 3.1
MakerZ.aiMistral AI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025August 13, 2025
Context window205K131K
Input $/Mtok$0.60$0.40
Output $/Mtok$2.20$2.00
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 Medium 3.1 for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding. Pick Mistral Medium 3.1 for output tokens 1.1x cheaper ($2.00 vs $2.20 per million) and work measured by MGSM (Vals AI) or LMArena Text: mathematical work, where it leads.

Which is cheaper, GLM 4.7 or Mistral Medium 3.1?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Mistral Medium 3.1?

GLM 4.7, with 205K tokens against 131K.

How many benchmarks is the GLM 4.7 vs Mistral Medium 3.1 comparison based on?

57 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 Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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