Mercury 2 vs Saba
Mercury 2 leads Saba on the Artificial Analysis Intelligence Index, 10.8 to 6.5, and Saba costs 1.3x less per output token ($0.60 vs $0.75 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
Mercury 2 or Saba: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mercury 2 for a larger context window (128K against 33K tokens).
- Shared results
- 3
- Mercury 2 only
- 39
- 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
Mercury 2
Mercury 2 leads, 10.8 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
No call
Only Mercury 2 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
Mercury 2
Mercury 2 takes 128K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
Mercury 2 $0.0006, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- Mercury 2 not benchmarked, Saba not benchmarked.
- Widest category
- Reasoning, 23.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Mercury 2 77.0% against Saba 42.4%, 34.6 points apart on a 0 to 100 scale.
- Output price
- Mercury 2 $0.75 against Saba $0.60 per million output tokens, 1.3x apart.
- Context window
- Mercury 2 takes 128K 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 | Mercury 2 | Bars | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| LMArena WebDev | 1171 | Not measured | Only Mercury 2 measured |
| AA Coding Index | 31.1 | Not measured | Only Mercury 2 measured |
| SciCode | 37.7% | Not measured | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | Not measured | Only Mercury 2 measured |
| WeirdML | 43.2% | Not measured | Only Mercury 2 measured |
| ALE-Bench | 785.6 | Not measured | Only Mercury 2 measured |
| LMArena Text: coding | 1390 | Not measured | Only Mercury 2 measured |
| LMArena Text: software and IT | 1385 | Not measured | Only Mercury 2 measured |
| LMArena WebDev: React | 1160 | Not measured | Only Mercury 2 measured |
| LMArena WebDev: HTML | 1199 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 6.5 | Mercury 2 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Mercury 2 measured |
| τ²-Bench Telecom | 70.8% | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 77.0% | 42.4% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 4.3% | Mercury 2 |
| LMArena Text: hard prompts | 1362 | Not measured | Only Mercury 2 measured |
| LMArena Text: expert | 1358 | Not measured | Only Mercury 2 measured |
| LMArena Text: hard prompts in English | 1374 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 17 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 61.1% | Only Saba measured |
| LMArena Text: science | 1343 | Not measured | Only Mercury 2 measured |
| LMArena Text: medicine and healthcare | 1363 | Not measured | Only Mercury 2 measured |
| LMArena Text: business and finance | 1349 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | Not measured | Only Mercury 2 measured |
| Design Arena: UI components | 998 | Not measured | Only Mercury 2 measured |
| Design Arena: game dev | 985 | Not measured | Only Mercury 2 measured |
| Design Arena: data viz | 1009 | Not measured | Only Mercury 2 measured |
| Design Arena: 3D | 983 | Not measured | Only Mercury 2 measured |
| Design Arena: all code categories | 1022 | Not measured | Only Mercury 2 measured |
| Design Arena: SVG | 998 | Not measured | Only Mercury 2 measured |
| Design Arena: ASCII art | 988 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | Mercury 2 | Saba | Leader |
|---|---|---|---|
| IFBench | 69.8% | Not measured | Only Mercury 2 measured |
| Long context reasoning (AA-LCR) | 43.7% | Not measured | Only Mercury 2 measured |
| LMArena Text | 1354 | Not measured | Only Mercury 2 measured |
| LMArena Text: instruction following | 1328 | Not measured | Only Mercury 2 measured |
| LMArena Text: creative writing | 1286 | Not measured | Only Mercury 2 measured |
| LMArena Text: English | 1378 | Not measured | Only Mercury 2 measured |
| LMArena Text: non-English | 1330 | Not measured | Only Mercury 2 measured |
| LMArena Text: Chinese | 1414 | Not measured | Only Mercury 2 measured |
| LMArena Text: Russian | 1304 | Not measured | Only Mercury 2 measured |
| LMArena Text: multi-turn | 1357 | Not measured | Only Mercury 2 measured |
| LMArena Text: longer queries | 1329 | Not measured | Only Mercury 2 measured |
| LMArena Text: writing and language | 1300 | Not measured | Only Mercury 2 measured |
| LMArena Text: entertainment and media | 1314 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 33 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2 | 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 | Inception Mercury 2 | Mistral Saba |
|---|---|---|
| Maker | Inception | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | March 4, 2026 | February 17, 2025 |
| Context window | 128K | 33K |
| Input $/Mtok | $0.25 | $0.20 |
| Output $/Mtok | $0.75 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2 better than Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mercury 2 for a larger context window (128K against 33K tokens).
Which is cheaper, Mercury 2 or Saba?
Mercury 2 costs $0.25 per million input tokens and $0.75 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, Mercury 2 or Saba?
Mercury 2, with 128K tokens against 33K.
How many benchmarks is the Mercury 2 vs Saba comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. Mercury 2's coding index rests on 1 independent benchmark and Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.