Saba vs Sonar
Sonar leads Saba on the Artificial Analysis Intelligence Index, 7.7 to 6.5, and Saba costs 1.7x less per output token ($0.60 vs $1.00 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
Saba or Sonar: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Saba for output tokens 1.7x cheaper ($0.60 vs $1.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 4
- Saba only
- 0
- Sonar only
- 1
- 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
Sonar
Sonar leads, 7.7 against 6.5.
Same board, both measured
Long documents
Listed context window
Sonar
Sonar takes 127K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
Saba $0.0005, Sonar $0.0015 per turn.
List prices
At a glance
- Coding index
- Saba not benchmarked, Sonar not benchmarked.
- Widest category
- Knowledge, 7.8 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Saba 61.1% against Sonar 68.9%, 7.8 points apart on a 0 to 100 scale.
- Output price
- Saba $0.60 against Sonar $1.00 per million output tokens, 1.7x apart.
- Context window
- Saba takes 33K tokens, Sonar 127K.
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 | Sonar | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | Saba | Sonar | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.5% | Only Sonar measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Saba | Sonar | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | 7.7 | Sonar |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Saba | Sonar | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Saba | Sonar | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.4% | 47.1% | Sonar |
| Humanity's Last Exam (Artificial Analysis run) | 4.3% | 4.9% | Sonar |
| 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 | Sonar | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Saba | Sonar | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 61.1% | 68.9% | Sonar |
| 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 | Sonar | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Saba | Sonar | Leader |
|---|---|---|---|
| Neither model has a result on 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Saba | Sonar | 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 | Perplexity Sonar |
|---|---|---|
| Maker | Mistral AI | Perplexity |
| Weights | No public weights listed | No public weights listed |
| Released | February 17, 2025 | January 27, 2025 |
| Context window | 33K | 127K |
| Input $/Mtok | $0.20 | $1.00 |
| Output $/Mtok | $0.60 | $1.00 |
| 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 Sonar for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Saba for output tokens 1.7x cheaper ($0.60 vs $1.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, Saba or Sonar?
Saba costs $0.20 per million input tokens and $0.60 per million output tokens; Sonar costs $1.00 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Saba or Sonar?
Sonar, with 127K tokens against 33K.
How many benchmarks is the Saba vs Sonar 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 Sonar's on 0. Where only one model has a result, the page says so instead of naming a winner.