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GLM 4.5V vs Mistral Medium 3.5

Mistral Medium 3.5 leads GLM 4.5V on the Artificial Analysis Intelligence Index, 14.2 to 7.6, and GLM 4.5V costs 4.2x less per output token ($1.80 vs $7.50 per million). Updated October 21, 2026; 37 benchmarks have a result for both.

Replace GLM 4.5V Replace Mistral Medium 3.5
GLM 4.5VZ.ai
Coding
No data
Overall
No data
Output $/M
$1.80
1
Mistral Medium 3.5Mistral AI
Coding
25.5
Overall
37.3
Output $/M
$7.50
36
vs boards won of 37 shared

GLM 4.5V or Mistral Medium 3.5: which to pick

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.

Pick GLM 4.5V for output tokens 4.2x cheaper ($1.80 vs $7.50 per million) and work measured by Kagi LLM Benchmark, where it leads.

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMistral Medium 3.5Mistral AI25.5/1003 independent benchmarks$6.02 per solved task
Shared results
37
GLM 4.5V only
4
Mistral Medium 3.5 only
44
Like-for-like categories
7 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 4.5V not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
Widest category
Agentic work, 71.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5V 22.5% against Mistral Medium 3.5 94.2%, 71.6 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against Mistral Medium 3.5 $7.50 per million output tokens, 4.2x apart.
Context window
GLM 4.5V takes 66K tokens, Mistral Medium 3.5 262K.

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 4.5VBarsMistral Medium 3.5Ties
Coding03
Intelligence01
Agentic work01
Reasoning17
Math02
Knowledge04
General018
All shared boards136
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
LMArena WebDevNot measured1264Only Mistral Medium 3.5 measured
AA Coding IndexNot measured46.9Only Mistral Medium 3.5 measured
SWE-bench Verified (Vals AI)Not measured66.4%Only Mistral Medium 3.5 measured
Vibe Code Bench (Vals AI)Not measured2.9%Only Mistral Medium 3.5 measured
Code migration (Vals AI)Not measured5.1%Only Mistral Medium 3.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured39.0%Only Mistral Medium 3.5 measured
LiveCodeBench (Artificial Analysis run)60.4%Not measuredOnly GLM 4.5V measured
SciCodeNot measured40.2%Only Mistral Medium 3.5 measured
Terminal-Bench Hard (Artificial Analysis run)6.8%33.3%Mistral Medium 3.5
ALE-BenchNot measured764.0Only Mistral Medium 3.5 measured
LMArena Text: coding13471460Mistral Medium 3.5
LMArena Text: software and IT13531458Mistral Medium 3.5
LMArena WebDev: ReactNot measured1254Only Mistral Medium 3.5 measured
LMArena WebDev: HTMLNot measured1308Only Mistral Medium 3.5 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
AA Intelligence Index7.614.2Mistral Medium 3.5
Epoch Capabilities IndexNot measured141.4Only Mistral Medium 3.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
AA Agentic IndexNot measured7.0Only Mistral Medium 3.5 measured
Finance Agent (Vals AI)Not measured32.1%Only Mistral Medium 3.5 measured
Tax Agent Bench (Vals AI)Not measured4.5%Only Mistral Medium 3.5 measured
Legal Research Bench (Vals AI)Not measured9.1%Only Mistral Medium 3.5 measured
Excel Modeling Benchmark (Vals AI)Not measured11.2%Only Mistral Medium 3.5 measured
τ²-Bench Telecom22.5%94.2%Mistral Medium 3.5
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index17
Reasoning benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
GPQA DiamondNot measured34.9%Only Mistral Medium 3.5 measured
GPQA Diamond (Artificial Analysis run)68.4%74.8%Mistral Medium 3.5
Humanity's Last Exam (Artificial Analysis run)6.3%13.8%Mistral Medium 3.5
LMArena Text: hard prompts13351436Mistral Medium 3.5
LMArena Text: expert13531432Mistral Medium 3.5
LMArena Text: hard prompts in English13571445Mistral Medium 3.5
LMArena Vision: diagrams11511254Mistral Medium 3.5
LMArena Vision: homework11631230Mistral Medium 3.5
Kagi LLM Benchmark59.8%41.4%GLM 4.5V
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
AA Math Index73.0Not measuredOnly GLM 4.5V measured
AIME 2025 (Artificial Analysis run)73.0%Not measuredOnly GLM 4.5V measured
ProofBenchNot measured9.0%Only Mistral Medium 3.5 measured
LMArena Text: math13541431Mistral Medium 3.5
LMArena Text: mathematical work13361440Mistral Medium 3.5
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
MMLU ProNot measured75.3%Only Mistral Medium 3.5 measured
CaseLaw (Vals AI)Not measured44.2%Only Mistral Medium 3.5 measured
CorpFin (Vals AI)Not measured58.8%Only Mistral Medium 3.5 measured
TaxEval (Vals AI)Not measured68.0%Only Mistral Medium 3.5 measured
MortgageTax (Vals AI)Not measured28.9%Only Mistral Medium 3.5 measured
MedCode (Vals AI)Not measured33.8%Only Mistral Medium 3.5 measured
MMLU Pro (Artificial Analysis run)78.8%Not measuredOnly GLM 4.5V measured
LMArena Text: science13301428Mistral Medium 3.5
LMArena Text: medicine and healthcare13491409Mistral Medium 3.5
LMArena Text: legal and government13381437Mistral Medium 3.5
LMArena Text: business and finance13091426Mistral Medium 3.5
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
Design Arena: websitesNot measured1142Only Mistral Medium 3.5 measured
Design Arena: UI componentsNot measured1079Only Mistral Medium 3.5 measured
Design Arena: game devNot measured1079Only Mistral Medium 3.5 measured
Design Arena: data vizNot measured1120Only Mistral Medium 3.5 measured
Design Arena: 3DNot measured1061Only Mistral Medium 3.5 measured
Design Arena: all code categoriesNot measured1121Only Mistral Medium 3.5 measured
Design Arena: SVGNot measured950Only Mistral Medium 3.5 measured
Design Arena: ASCII artNot measured1063Only Mistral Medium 3.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
SAGE (Vals AI)Not measured37.6%Only Mistral Medium 3.5 measured
MedScribe (Vals AI)Not measured67.7%Only Mistral Medium 3.5 measured
Vals Multimodal IndexNot measured34.8%Only Mistral Medium 3.5 measured
IFBench34.2%68.8%Mistral Medium 3.5
Long context reasoning (AA-LCR)0.0%69.3%Mistral Medium 3.5
DTBenchNot measured75.5%Only Mistral Medium 3.5 measured
LMCANot measured26.1%Only Mistral Medium 3.5 measured
Surface Evolver BenchNot measured26.9%Only Mistral Medium 3.5 measured
LMArena Text13331421Mistral Medium 3.5
LMArena Text: instruction following13111415Mistral Medium 3.5
LMArena Text: creative writing12941374Mistral Medium 3.5
LMArena Vision11541222Mistral Medium 3.5
LMArena Text: English13621433Mistral Medium 3.5
LMArena Text: non-English13031404Mistral Medium 3.5
LMArena Text: Chinese13371444Mistral Medium 3.5
LMArena Text: GermanNot measured1453Only Mistral Medium 3.5 measured
LMArena Text: KoreanNot measured1384Only Mistral Medium 3.5 measured
LMArena Text: FrenchNot measured1448Only Mistral Medium 3.5 measured
LMArena Text: Spanish13361411Mistral Medium 3.5
LMArena Text: Russian12981395Mistral Medium 3.5
LMArena Text: Polish13021430Mistral Medium 3.5
LMArena Text: multi-turn13331423Mistral Medium 3.5
LMArena Text: longer queries13051415Mistral Medium 3.5
LMArena Text: writing and language13021384Mistral Medium 3.5
LMArena Text: entertainment and media13031382Mistral Medium 3.5
LMArena Vision: English11831240Mistral Medium 3.5
LMArena Vision: ChineseNot measured1244Only Mistral Medium 3.5 measured
LMArena Vision: reading text11511233Mistral Medium 3.5
LMArena Vision: humorNot measured1216Only Mistral Medium 3.5 measured
LMArena Vision: creative writingNot measured1198Only Mistral Medium 3.5 measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5V and Mistral Medium 3.5
BenchmarkGLM 4.5VMistral Medium 3.5Leader
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 4.5V$0.0015Fits in one request
Mistral Medium 3.5$0.0053Fits in one request

GLM 4.5V costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
Mistral Medium 3.5$0.098Fits in one request

GLM 4.5V costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
Mistral Medium 3.5$0.375Fits in one request

GLM 4.5V 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 4.5V and Mistral Medium 3.5
SpecificationZ.ai GLM 4.5VMistral Medium 3.5
MakerZ.aiMistral AI
WeightsOpen weightsNo public weights listed
ReleasedAugust 11, 2025April 30, 2026
Context window66K262K
Input $/Mtok$0.60$1.50
Output $/Mtok$1.80$7.50
Coding indexNot benchmarked25.5
Independent measurements behind it03
Cost per solved taskNot computable$6.02
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than Mistral Medium 3.5 for coding?

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Pick GLM 4.5V for output tokens 4.2x cheaper ($1.80 vs $7.50 per million) and work measured by Kagi LLM Benchmark, where it leads.

Which is cheaper, GLM 4.5V or Mistral Medium 3.5?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, GLM 4.5V or Mistral Medium 3.5?

Mistral Medium 3.5, with 262K tokens against 66K.

How many benchmarks is the GLM 4.5V vs Mistral Medium 3.5 comparison based on?

37 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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