GLM 4.5V vs Mixtral 8x22B Instruct
GLM 4.5V leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 7.6 to 5.7, and GLM 4.5V costs 3.3x less per output token ($1.80 vs $6.00 per million). Updated October 21, 2026; 5 benchmarks have a result for both.
GLM 4.5V or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5V for output tokens 3.3x cheaper ($1.80 vs $6.00 per million).
- Shared results
- 5
- GLM 4.5V only
- 36
- Mixtral 8x22B Instruct only
- 3
- 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
GLM 4.5V
GLM 4.5V leads, 7.6 against 5.7.
Same board, both measured
Long documents
Listed context window
No call
Both take 66K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5V
GLM 4.5V $0.0015, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, Mixtral 8x22B Instruct not benchmarked.
- Widest category
- Coding, 45.6 points apart on average over 1 shared benchmark.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): GLM 4.5V 60.4% against Mixtral 8x22B Instruct 14.8%, 45.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against Mixtral 8x22B Instruct $6.00 per million output tokens, 3.3x apart.
- Context window
- Both take 66K tokens.
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 | Mixtral 8x22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 5 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 60.4% | 14.8% | GLM 4.5V |
| 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 |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 5.7 | GLM 4.5V |
| Epoch Capabilities Index | Not measured | 122.0 | Only Mixtral 8x22B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | 33.2% | GLM 4.5V |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | 4.0% | GLM 4.5V |
| 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 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | 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 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | 53.7% | GLM 4.5V |
| 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 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | 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 |
| DTBench | Not measured | 55.1% | Only Mixtral 8x22B Instruct 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 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Mixtral 8x22B Instruct | 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
GLM 4.5V costs less
Repository review
50K input + 3K output tokens
GLM 4.5V costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Z.ai GLM 4.5V | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 11, 2025 | April 17, 2024 |
| Context window | 66K | 66K |
| Input $/Mtok | $0.60 | $2.00 |
| Output $/Mtok | $1.80 | $6.00 |
| 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 Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5V for output tokens 3.3x cheaper ($1.80 vs $6.00 per million).
Which is cheaper, GLM 4.5V or Mixtral 8x22B Instruct?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Mixtral 8x22B Instruct costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or Mixtral 8x22B Instruct?
Neither: both take 66K tokens.
How many benchmarks is the GLM 4.5V vs Mixtral 8x22B Instruct comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Mixtral 8x22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.