R1 0528 vs Saba
R1 0528 leads Saba on the Artificial Analysis Intelligence Index, 13.1 to 6.5, and Saba costs 3.6x less per output token ($0.60 vs $2.18 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
R1 0528 or Saba: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick R1 0528 for a larger context window (164K against 33K tokens).
- Shared results
- 4
- R1 0528 only
- 56
- Saba only
- 0
- 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
R1 0528
R1 0528 leads, 13.1 against 6.5.
Same board, both measured
Web design
Design Arena: websites
No call
Only R1 0528 has a Design Arena: websites result, 1149.
Directional only
Long documents
Listed context window
R1 0528
R1 0528 takes 164K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
R1 0528 $0.0016, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, Saba not benchmarked.
- Widest category
- Reasoning, 25.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): R1 0528 81.3% against Saba 42.4%, 38.9 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against Saba $0.60 per million output tokens, 3.6x apart.
- Context window
- R1 0528 takes 164K 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 | R1 0528 | Bars | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | Not measured | Only R1 0528 measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | Not measured | Only R1 0528 measured |
| LMArena Text: coding | 1427 | Not measured | Only R1 0528 measured |
| LMArena Text: software and IT | 1435 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 6.5 | R1 0528 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | Not measured | Only R1 0528 measured |
| METR time horizons | 53.8% | Not measured | Only R1 0528 measured |
| DeepResearch Bench | 35.1% | Not measured | Only R1 0528 measured |
| BrowseComp-Plus | 16.4% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 42.4% | R1 0528 |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 4.3% | R1 0528 |
| GPQA Diamond (Epoch AI run) | 76.3% | Not measured | Only R1 0528 measured |
| ARC-AGI | 21.2% | Not measured | Only R1 0528 measured |
| ARC-AGI-2 | 1.1% | Not measured | Only R1 0528 measured |
| SimpleBench | 40.8% | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only R1 0528 measured |
| LMArena Text: expert | 1394 | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts in English | 1422 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| AA Math Index | 76.0 | Not measured | Only R1 0528 measured |
| AIME 2025 (Artificial Analysis run) | 76.0% | Not measured | Only R1 0528 measured |
| OTIS Mock AIME | 66.4% | Not measured | Only R1 0528 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | Not measured | Only R1 0528 measured |
| LMArena Text: mathematical work | 1415 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.9% | 61.1% | R1 0528 |
| LMArena Text: science | 1430 | Not measured | Only R1 0528 measured |
| LMArena Text: medicine and healthcare | 1445 | Not measured | Only R1 0528 measured |
| LMArena Text: legal and government | 1419 | Not measured | Only R1 0528 measured |
| LMArena Text: business and finance | 1402 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | Not measured | Only R1 0528 measured |
| Design Arena: UI components | 1103 | Not measured | Only R1 0528 measured |
| Design Arena: game dev | 1097 | Not measured | Only R1 0528 measured |
| Design Arena: data viz | 1182 | Not measured | Only R1 0528 measured |
| Design Arena: 3D | 1111 | Not measured | Only R1 0528 measured |
| Design Arena: all code categories | 1139 | Not measured | Only R1 0528 measured |
| Design Arena: SVG | 1039 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | R1 0528 | Saba | Leader |
|---|---|---|---|
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | Not measured | Only R1 0528 measured |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| LMArena Text | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: instruction following | 1382 | Not measured | Only R1 0528 measured |
| LMArena Text: creative writing | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: English | 1437 | Not measured | Only R1 0528 measured |
| LMArena Text: non-English | 1412 | Not measured | Only R1 0528 measured |
| LMArena Text: Chinese | 1442 | Not measured | Only R1 0528 measured |
| LMArena Text: German | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: Japanese | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: Korean | 1360 | Not measured | Only R1 0528 measured |
| LMArena Text: French | 1417 | Not measured | Only R1 0528 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only R1 0528 measured |
| LMArena Text: Russian | 1423 | Not measured | Only R1 0528 measured |
| LMArena Text: Polish | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: multi-turn | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: longer queries | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: writing and language | 1402 | Not measured | Only R1 0528 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | 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 | DeepSeek R1 0528 | Mistral Saba |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | May 28, 2025 | February 17, 2025 |
| Context window | 164K | 33K |
| Input $/Mtok | $0.50 | $0.20 |
| Output $/Mtok | $2.18 | $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 R1 0528 better than Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for a larger context window (164K against 33K tokens).
Which is cheaper, R1 0528 or Saba?
R1 0528 costs $0.50 per million input tokens and $2.18 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, R1 0528 or Saba?
R1 0528, with 164K tokens against 33K.
How many benchmarks is the R1 0528 vs Saba comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. R1 0528'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.