Granite 4.0 Micro vs Mixtral 8x22B Instruct
Mixtral 8x22B Instruct leads Granite 4.0 Micro on the Artificial Analysis Intelligence Index, 5.7 to 5.1, and Granite 4.0 Micro costs 53.6x less per output token ($0.112 vs $6.00 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
Granite 4.0 Micro or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Granite 4.0 Micro for output tokens 53.6x cheaper ($0.112 vs $6.00 per million) and a larger context window (131K against 66K tokens).
- Shared results
- 5
- Granite 4.0 Micro only
- 8
- 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
Mixtral 8x22B Instruct
Mixtral 8x22B Instruct leads, 5.7 against 5.1.
Same board, both measured
Long documents
Listed context window
Granite 4.0 Micro
Granite 4.0 Micro takes 131K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
Granite 4.0 Micro $0.0001, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- Granite 4.0 Micro not benchmarked, Mixtral 8x22B Instruct not benchmarked.
- Widest category
- Knowledge, 9.0 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Granite 4.0 Micro 44.7% against Mixtral 8x22B Instruct 53.7%, 9.0 points apart on a 0 to 100 scale.
- Output price
- Granite 4.0 Micro $0.112 against Mixtral 8x22B Instruct $6.00 per million output tokens, 53.6x apart.
- Context window
- Granite 4.0 Micro takes 131K 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 | Granite 4.0 Micro | Bars | Mixtral 8x22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 3 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 18.0% | 14.8% | Granite 4.0 Micro |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Granite 4.0 Micro measured |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.1 | 5.7 | Mixtral 8x22B Instruct |
| 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 | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 12.6% | Not measured | Only Granite 4.0 Micro 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 | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 33.6% | 33.2% | Granite 4.0 Micro |
| Humanity's Last Exam (Artificial Analysis run) | 5.0% | 4.0% | Granite 4.0 Micro |
| GPQA Diamond (Epoch AI run) | 28.3% | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only Granite 4.0 Micro measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only Granite 4.0 Micro measured |
| OTIS Mock AIME | 2.8% | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 44.7% | 53.7% | Mixtral 8x22B Instruct |
| Neither model has a result on the other 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Granite 4.0 Micro | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| IFBench | 24.8% | Not measured | Only Granite 4.0 Micro measured |
| Long context reasoning (AA-LCR) | 7.0% | Not measured | Only Granite 4.0 Micro 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 | Granite 4.0 Micro | 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
Granite 4.0 Micro costs less
Repository review
50K input + 3K output tokens
Granite 4.0 Micro costs less
Agent loop
200K input + 10K output tokens
Granite 4.0 Micro 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 | IBM Granite 4.0 Micro | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | IBM Granite | Mistral AI |
| Weights | Open weights | Open weights |
| Released | October 20, 2025 | April 17, 2024 |
| Context window | 131K | 66K |
| Input $/Mtok | $0.017 | $2.00 |
| Output $/Mtok | $0.112 | $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 Granite 4.0 Micro better than Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Granite 4.0 Micro for output tokens 53.6x cheaper ($0.112 vs $6.00 per million) and a larger context window (131K against 66K tokens).
Which is cheaper, Granite 4.0 Micro or Mixtral 8x22B Instruct?
Granite 4.0 Micro costs $0.017 per million input tokens and $0.112 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, Granite 4.0 Micro or Mixtral 8x22B Instruct?
Granite 4.0 Micro, with 131K tokens against 66K.
How many benchmarks is the Granite 4.0 Micro vs Mixtral 8x22B Instruct comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. Granite 4.0 Micro'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.