LongCat 2.0 vs Saba
LongCat 2.0 leads Saba on the Artificial Analysis Intelligence Index, 19.1 to 6.5, and Saba costs 2.0x less per output token ($0.60 vs $1.20 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
LongCat 2.0 or Saba: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick LongCat 2.0 for a larger context window (1.05M against 33K tokens).
- Shared results
- 3
- LongCat 2.0 only
- 5
- Saba only
- 1
- 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
LongCat 2.0
LongCat 2.0 leads, 19.1 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only LongCat 2.0 has a AA Agentic Index result, 14.0.
Directional only
Long documents
Listed context window
LongCat 2.0
LongCat 2.0 takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
LongCat 2.0 $0.0009, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- LongCat 2.0 not benchmarked, Saba not benchmarked.
- Widest category
- Reasoning, 32.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): LongCat 2.0 78.0% against Saba 42.4%, 35.6 points apart on a 0 to 100 scale.
- Output price
- LongCat 2.0 $1.20 against Saba $0.60 per million output tokens, 2.0x apart.
- Context window
- LongCat 2.0 takes 1.05M 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 | LongCat 2.0 | Bars | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | LongCat 2.0 | Saba | Leader |
|---|---|---|---|
| AA Coding Index | 45.3 | Not measured | Only LongCat 2.0 measured |
| SciCode | 36.3% | Not measured | Only LongCat 2.0 measured |
| ALE-Bench | 793.5 | Not measured | Only LongCat 2.0 measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | LongCat 2.0 | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 19.1 | 6.5 | LongCat 2.0 |
| 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 | LongCat 2.0 | Saba | Leader |
|---|---|---|---|
| AA Agentic Index | 14.0 | Not measured | Only LongCat 2.0 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 | LongCat 2.0 | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 78.0% | 42.4% | LongCat 2.0 |
| Humanity's Last Exam (Artificial Analysis run) | 33.7% | 4.3% | LongCat 2.0 |
| 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 | LongCat 2.0 | Saba | 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 | LongCat 2.0 | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 61.1% | Only Saba measured |
| 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 | LongCat 2.0 | Saba | 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 | LongCat 2.0 | Saba | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 65.0% | Not measured | Only LongCat 2.0 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 | LongCat 2.0 | 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 | Meituan LongCat 2.0 | Mistral Saba |
|---|---|---|
| Maker | Meituan | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 20, 2026 | February 17, 2025 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $0.30 | $0.20 |
| Output $/Mtok | $1.20 | $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 LongCat 2.0 better than Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick LongCat 2.0 for a larger context window (1.05M against 33K tokens).
Which is cheaper, LongCat 2.0 or Saba?
LongCat 2.0 costs $0.30 per million input tokens and $1.20 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, LongCat 2.0 or Saba?
LongCat 2.0, with 1.05M tokens against 33K.
How many benchmarks is the LongCat 2.0 vs Saba comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. LongCat 2.0'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.