Saba vs Mixtral 8x22B Instruct
Saba leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 6.5 to 5.7, and Saba costs 10.0x less per output token ($0.60 vs $6.00 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
Saba or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Saba for output tokens 10.0x cheaper ($0.60 vs $6.00 per million).
- Shared results
- 4
- Saba only
- 0
- Mixtral 8x22B Instruct only
- 4
- Like-for-like categories
- 3 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
Saba
Saba leads, 6.5 against 5.7.
Same board, both measured
Long documents
Listed context window
Mixtral 8x22B Instruct
Mixtral 8x22B Instruct takes 66K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
Saba $0.0005, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- Saba not benchmarked, Mixtral 8x22B Instruct not benchmarked.
- Widest category
- Knowledge, 7.4 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Saba 42.4% against Mixtral 8x22B Instruct 33.2%, 9.2 points apart on a 0 to 100 scale.
- Output price
- Saba $0.60 against Mixtral 8x22B Instruct $6.00 per million output tokens, 10.0x apart.
- Context window
- Saba takes 33K 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 | Saba | Bars | Mixtral 8x22B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 14.8% | Only Mixtral 8x22B Instruct measured |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | 5.7 | Saba |
| Epoch Capabilities Index | Not measured | 122.0 | Only Mixtral 8x22B Instruct measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.4% | 33.2% | Saba |
| Humanity's Last Exam (Artificial Analysis run) | 4.3% | 4.0% | Saba |
| 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 | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 61.1% | 53.7% | Saba |
| 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 | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Saba | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| DTBench | Not measured | 55.1% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Saba | 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
Saba costs less
Repository review
50K input + 3K output tokens
Saba costs less
Agent loop
200K input + 10K output tokens
Saba 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 | Mistral Saba | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | Mistral AI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | February 17, 2025 | April 17, 2024 |
| Context window | 33K | 66K |
| Input $/Mtok | $0.20 | $2.00 |
| Output $/Mtok | $0.60 | $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 Saba better than Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Saba for output tokens 10.0x cheaper ($0.60 vs $6.00 per million).
Which is cheaper, Saba or Mixtral 8x22B Instruct?
Saba costs $0.20 per million input tokens and $0.60 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, Saba or Mixtral 8x22B Instruct?
Mixtral 8x22B Instruct, with 66K tokens against 33K.
How many benchmarks is the Saba vs Mixtral 8x22B Instruct comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. Saba'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.