GLM 4.6V vs Mistral Small 3.1 24B
Mistral Small 3.1 24B costs 1.6x less per output token than GLM 4.6V ($0.555 vs $0.90 per million). Updated October 21, 2026; 27 benchmarks have a result for both.
GLM 4.6V or Mistral Small 3.1 24B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for a larger context window (131K against 128K tokens) and work measured by LMArena Vision: homework or LMArena Vision: creative writing, where it leads.
- Shared results
- 27
- GLM 4.6V only
- 11
- Mistral Small 3.1 24B only
- 11
- Like-for-like categories
- 4 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.6V has a AA Intelligence Index result, 11.2.
Directional only
Long documents
Listed context window
GLM 4.6V
GLM 4.6V takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.1 24B
GLM 4.6V $0.0008, Mistral Small 3.1 24B $0.0006 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Mistral Small 3.1 24B not benchmarked.
- Widest category
- Knowledge, 19.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.6V 1425 against Mistral Small 3.1 24B 1253, 28.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Mistral Small 3.1 24B $0.555 per million output tokens, 1.6x apart.
- Context window
- GLM 4.6V takes 131K tokens, Mistral Small 3.1 24B 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.
| Category | GLM 4.6V | Bars | Mistral Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 4 | 1 | ||
| Knowledge | 4 | 0 | ||
| General | 15 | 1 | ||
| All shared boards | 25 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | Not measured | Only GLM 4.6V measured |
| LMArena Text: coding | 1389 | 1309 | GLM 4.6V |
| LMArena Text: software and IT | 1391 | 1301 | GLM 4.6V |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | Not measured | Only GLM 4.6V measured |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | Not measured | Only GLM 4.6V measured |
| LMArena Text: hard prompts | 1367 | 1278 | GLM 4.6V |
| LMArena Text: expert | 1371 | 1257 | GLM 4.6V |
| LMArena Text: hard prompts in English | 1391 | 1290 | GLM 4.6V |
| LMArena Vision: diagrams | 1182 | 1119 | GLM 4.6V |
| LMArena Vision: homework | 1155 | 1158 | Mistral Small 3.1 24B |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| LMArena Text: math | Not measured | 1261 | Only Mistral Small 3.1 24B measured |
| LMArena Text: mathematical work | Not measured | 1271 | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| LMArena Text: science | 1389 | 1265 | GLM 4.6V |
| LMArena Text: medicine and healthcare | 1389 | 1267 | GLM 4.6V |
| LMArena Text: legal and government | 1393 | 1271 | GLM 4.6V |
| LMArena Text: business and finance | 1359 | 1257 | GLM 4.6V |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index151
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| IFBench | 30.1% | Not measured | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | 48.7% | Not measured | Only GLM 4.6V measured |
| LMArena Text | 1376 | 1277 | GLM 4.6V |
| LMArena Text: instruction following | 1350 | 1263 | GLM 4.6V |
| LMArena Text: creative writing | 1347 | 1252 | GLM 4.6V |
| LMArena Vision | 1164 | 1136 | GLM 4.6V |
| LMArena Text: English | 1395 | 1295 | GLM 4.6V |
| LMArena Text: non-English | 1358 | 1255 | GLM 4.6V |
| LMArena Text: Chinese | 1425 | 1253 | GLM 4.6V |
| LMArena Text: German | Not measured | 1266 | Only Mistral Small 3.1 24B measured |
| LMArena Text: Japanese | Not measured | 1208 | Only Mistral Small 3.1 24B measured |
| LMArena Text: Korean | Not measured | 1207 | Only Mistral Small 3.1 24B measured |
| LMArena Text: French | Not measured | 1273 | Only Mistral Small 3.1 24B measured |
| LMArena Text: Spanish | Not measured | 1284 | Only Mistral Small 3.1 24B measured |
| LMArena Text: Russian | 1339 | 1263 | GLM 4.6V |
| LMArena Text: Polish | Not measured | 1242 | Only Mistral Small 3.1 24B measured |
| LMArena Text: multi-turn | 1359 | 1270 | GLM 4.6V |
| LMArena Text: longer queries | 1357 | 1299 | GLM 4.6V |
| LMArena Text: writing and language | 1347 | 1268 | GLM 4.6V |
| LMArena Text: entertainment and media | 1343 | 1250 | GLM 4.6V |
| LMArena Vision: English | 1179 | 1157 | GLM 4.6V |
| LMArena Vision: Chinese | 1217 | 1155 | GLM 4.6V |
| LMArena Vision: reading text | 1171 | 1132 | GLM 4.6V |
| LMArena Vision: humor | Not measured | 1142 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: captioning | Not measured | 1153 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | 1113 | 1123 | Mistral Small 3.1 24B |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| 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
Mistral Small 3.1 24B costs less
Repository review
50K input + 3K output tokens
GLM 4.6V costs less
Agent loop
200K input + 10K output tokens
GLM 4.6V 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.
| Specification | Z.ai GLM 4.6V | Mistral Small 3.1 24B |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | March 17, 2025 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.30 | $0.351 |
| Output $/Mtok | $0.90 | $0.555 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Mistral Small 3.1 24B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for a larger context window (131K against 128K tokens) and work measured by LMArena Vision: homework or LMArena Vision: creative writing, where it leads.
Which is cheaper, GLM 4.6V or Mistral Small 3.1 24B?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or Mistral Small 3.1 24B?
GLM 4.6V, with 131K tokens against 128K.
How many benchmarks is the GLM 4.6V vs Mistral Small 3.1 24B comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Mistral Small 3.1 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.