GLM 4.5V vs Ministral 3 14B 2512
GLM 4.5V leads Ministral 3 14B 2512 on the Artificial Analysis Intelligence Index, 7.6 to 6.0, and Ministral 3 14B 2512 costs 9.0x less per output token ($0.20 vs $1.80 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GLM 4.5V or Ministral 3 14B 2512: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 1
- GLM 4.5V only
- 41
- Ministral 3 14B 2512 only
- 6
- Like-for-like categories
- 1 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
GLM 4.5V
GLM 4.5V leads, 7.6 against 6.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ministral 3 14B 2512 has a AA Agentic Index result, 1.1.
Directional only
Web design
Design Arena: websites
No call
Only Ministral 3 14B 2512 has a Design Arena: websites result, 1081.
Directional only
Long documents
Listed context window
Ministral 3 14B 2512
Ministral 3 14B 2512 takes 262K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ministral 3 14B 2512
GLM 4.5V $0.0015, Ministral 3 14B 2512 $0.0003 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, Ministral 3 14B 2512 not benchmarked.
- Widest category
- Intelligence, 1.6 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GLM 4.5V 7.6 against Ministral 3 14B 2512 6.0, 1.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against Ministral 3 14B 2512 $0.20 per million output tokens, 9.0x apart.
- Context window
- GLM 4.5V takes 66K tokens, Ministral 3 14B 2512 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.
| Category | GLM 4.5V | Bars | Ministral 3 14B 2512 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 14.4 | Only Ministral 3 14B 2512 measured |
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: coding | 1347 | Not measured | Only GLM 4.5V measured |
| LMArena Text: software and IT | 1353 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 6.0 | GLM 4.5V |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.1 | Only Ministral 3 14B 2512 measured |
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | Not measured | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts | 1335 | Not measured | Only GLM 4.5V measured |
| LMArena Text: expert | 1353 | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | 1357 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 59.8% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text: math | 1354 | Not measured | Only GLM 4.5V measured |
| LMArena Text: mathematical work | 1336 | Not measured | Only GLM 4.5V measured |
| MathArena | 10.5% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: science | 1330 | Not measured | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only GLM 4.5V measured |
| LMArena Text: legal and government | 1338 | Not measured | Only GLM 4.5V measured |
| LMArena Text: business and finance | 1309 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1081 | Only Ministral 3 14B 2512 measured |
| Design Arena: game dev | Not measured | 1037 | Only Ministral 3 14B 2512 measured |
| Design Arena: 3D | Not measured | 989 | Only Ministral 3 14B 2512 measured |
| Design Arena: all code categories | Not measured | 1064 | Only Ministral 3 14B 2512 measured |
| Neither model has a result on the other 5 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| IFBench | 34.2% | Not measured | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 0.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: instruction following | 1311 | Not measured | Only GLM 4.5V measured |
| LMArena Text: creative writing | 1294 | Not measured | Only GLM 4.5V measured |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | Not measured | Only GLM 4.5V measured |
| LMArena Text: non-English | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Chinese | 1337 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Spanish | 1336 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Russian | 1298 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Polish | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: longer queries | 1305 | Not measured | Only GLM 4.5V measured |
| LMArena Text: writing and language | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Ministral 3 14B 2512 | 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
Ministral 3 14B 2512 costs less
Repository review
50K input + 3K output tokens
Ministral 3 14B 2512 costs less
Agent loop
200K input + 10K output tokens
Ministral 3 14B 2512 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 4.5V | Mistral Ministral 3 14B 2512 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 11, 2025 | December 2, 2025 |
| Context window | 66K | 262K |
| Input $/Mtok | $0.60 | $0.20 |
| Output $/Mtok | $1.80 | $0.20 |
| 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 4.5V better than Ministral 3 14B 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GLM 4.5V or Ministral 3 14B 2512?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Ministral 3 14B 2512 costs $0.20 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or Ministral 3 14B 2512?
Ministral 3 14B 2512, with 262K tokens against 66K.
How many benchmarks is the GLM 4.5V vs Ministral 3 14B 2512 comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Ministral 3 14B 2512's on 0. Where only one model has a result, the page says so instead of naming a winner.