GLM 5 Turbo vs Mixtral 8x22B Instruct
GLM 5 Turbo leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 26.6 to 5.7, and GLM 5 Turbo costs 1.5x less per output token ($4.00 vs $6.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GLM 5 Turbo or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5 Turbo for output tokens 1.5x cheaper ($4.00 vs $6.00 per million) and a larger context window (203K against 66K tokens).
- Shared results
- 3
- GLM 5 Turbo only
- 13
- Mixtral 8x22B Instruct only
- 5
- Like-for-like categories
- 2 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 5 Turbo
GLM 5 Turbo leads, 26.6 against 5.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5 Turbo has a Design Arena: websites result, 1262.
Directional only
Long documents
Listed context window
GLM 5 Turbo
GLM 5 Turbo takes 203K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5 Turbo
GLM 5 Turbo $0.0032, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- GLM 5 Turbo not benchmarked, Mixtral 8x22B Instruct not benchmarked.
- Widest category
- Reasoning, 37.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GLM 5 Turbo 84.7% against Mixtral 8x22B Instruct 33.2%, 51.5 points apart on a 0 to 100 scale.
- Output price
- GLM 5 Turbo $4.00 against Mixtral 8x22B Instruct $6.00 per million output tokens, 1.5x apart.
- Context window
- GLM 5 Turbo takes 203K tokens, Mixtral 8x22B Instruct 66K.
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 5 Turbo | Bars | Mixtral 8x22B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GLM 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 14.8% | Only Mixtral 8x22B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GLM 5 Turbo measured |
| ALE-Bench | 634.0 | Not measured | Only GLM 5 Turbo measured |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.6 | 5.7 | GLM 5 Turbo |
| 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 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.5% | Not measured | Only GLM 5 Turbo 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 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.7% | 33.2% | GLM 5 Turbo |
| Humanity's Last Exam (Artificial Analysis run) | 27.8% | 4.0% | GLM 5 Turbo |
| Neither model has a result on the other 20 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 53.7% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1262 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: UI components | 1263 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: game dev | 1248 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: data viz | 1264 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: 3D | 1248 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: all code categories | 1262 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: SVG | 1206 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: ASCII art | 1160 | Not measured | Only GLM 5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GLM 5 Turbo | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| IFBench | 73.2% | Not measured | Only GLM 5 Turbo measured |
| Long context reasoning (AA-LCR) | 71.7% | Not measured | Only GLM 5 Turbo measured |
| DTBench | Not measured | 55.1% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | 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 5 Turbo costs less
Repository review
50K input + 3K output tokens
GLM 5 Turbo costs less
Agent loop
200K input + 10K output tokens
GLM 5 Turbo 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 5 Turbo | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | March 15, 2026 | April 17, 2024 |
| Context window | 203K | 66K |
| Input $/Mtok | $1.20 | $2.00 |
| Output $/Mtok | $4.00 | $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 5 Turbo better than Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5 Turbo for output tokens 1.5x cheaper ($4.00 vs $6.00 per million) and a larger context window (203K against 66K tokens).
Which is cheaper, GLM 5 Turbo or Mixtral 8x22B Instruct?
GLM 5 Turbo costs $1.20 per million input tokens and $4.00 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 5 Turbo or Mixtral 8x22B Instruct?
GLM 5 Turbo, with 203K tokens against 66K.
How many benchmarks is the GLM 5 Turbo vs Mixtral 8x22B Instruct comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GLM 5 Turbo'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.