Mistral Large 2407 vs Saba
Updated October 21, 2026. 4 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Mistral Large 2407 has the edge where the context window is larger, 131K against 33K tokens, and where the benchmark that matters is Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 4
- Mistral Large 2407 only
- 13
- Saba only
- 0
- Like-for-like categories
- 3 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Mistral Large 2407
Mistral Large 2407 leads, 7.6 against 6.5.
Same board, both measured
Long documents
Listed context window
Mistral Large 2407
Mistral Large 2407 takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
Mistral Large 2407 $0.0050, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Saba not benchmarked.
- Widest category
- Knowledge, 7.2 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Mistral Large 2407 68.3% against Saba 61.1%, 7.2 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 2407 $6.00 against Saba $0.60 per million output tokens, 10.0x apart.
- Context window
- Mistral Large 2407 takes 131K tokens, Saba 33K.
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 | Mistral Large 2407 | Bars | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 3 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 26.7% | Not measured | Only Mistral Large 2407 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 | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 6.5 | Mistral Large 2407 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | 42.4% | Mistral Large 2407 |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | 4.3% | Saba |
| GPQA Diamond (Epoch AI run) | 49.0% | Not measured | Only Mistral Large 2407 measured |
| SimpleBench | 22.5% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | Not measured | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | 0.0% | Not measured | Only Mistral Large 2407 measured |
| OTIS Mock AIME | 8.5% | Not measured | Only Mistral Large 2407 measured |
| MATH level 5 | 44.8% | Not measured | Only Mistral Large 2407 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 | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | 61.1% | Mistral Large 2407 |
| MMLU | 80.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Large 2407 | Saba | Leader |
|---|---|---|---|
| IFBench | 31.6% | Not measured | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | 2.0% | Not measured | Only Mistral Large 2407 measured |
| DTBench | 61.2% | Not measured | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | 69.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Saba | 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 Large 2407 | Mistral Saba |
|---|---|---|
| Maker | Mistral AI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | November 19, 2024 | February 17, 2025 |
| Context window | 131K | 33K |
| Input $/Mtok | $2.00 | $0.20 |
| Output $/Mtok | $6.00 | $0.60 |
| 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 Mistral Large 2407 better than Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Mistral Large 2407 has the edge where the context window is larger, 131K against 33K tokens, and where the benchmark that matters is Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Mistral Large 2407 or Saba?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Saba costs $0.20 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Saba?
Mistral Large 2407, with 131K tokens against 33K.
How many benchmarks is the Mistral Large 2407 vs Saba comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. Mistral Large 2407's coding index rests on 0 independent benchmarks and Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.