R1 0528 vs GPT-5.5
Updated October 21, 2026. 50 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 67.8 for GPT-5.5, though on 1 benchmark against 2.
Pick GPT-5.5 when the context window is larger, 1.05M against 164K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.
- Shared results
- 50
- R1 0528 only
- 9
- GPT-5.5 only
- 82
- Like-for-like categories
- 8 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, four public coding benchmarks
R1 0528
R1 0528 scores 71.4 against 67.8.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.5 has a GPQA Diamond result, 93.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.5 has a MMLU Pro result, 88.1%.
Directional only
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 against 1149.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
R1 0528
R1 0528 $0.0016, GPT-5.5 $0.020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.5 has the number, $1.65.
Directional only
At a glance
- Coding index
- R1 0528 71.4 from 1 benchmark, GPT-5.5 67.8 from 2 benchmarks.
- Widest category
- Agentic work, 38.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- ARC-AGI-2: R1 0528 1.1% against GPT-5.5 85.0%, 83.9 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.15 against GPT-5.5 $30.00 per million output tokens, 14.0x apart.
- Context window
- R1 0528 takes 164K tokens, GPT-5.5 1.05M.
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 | GPT-5.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 9 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 0 | 19 | ||
| All shared boards | 0 | 50 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveBench coding | Not measured | 85.1% | Only GPT-5.5 measured |
| LMArena WebDev | Not measured | 1513 | Only GPT-5.5 measured |
| AA Coding Index | Not measured | 74.9 | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | Not measured | 85.3% | Only GPT-5.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.6% | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 69.8% | Only GPT-5.5 measured |
| Code migration (Vals AI) | Not measured | 45.2% | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 76.4% | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | Not measured | 62.5% | Only GPT-5.5 measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| SciCode | Not measured | 56.1% | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | 60.6% | GPT-5.5 |
| Terminal-Bench | Not measured | 84.7% | Only GPT-5.5 measured |
| FrontierCode | Not measured | 43.0% | Only GPT-5.5 measured |
| GSO-Bench | Not measured | 40.2% | Only GPT-5.5 measured |
| WeirdML | 41.6% | 84.9% | GPT-5.5 |
| MirrorCode | Not measured | 10.0% | Only GPT-5.5 measured |
| ALE-Bench | 804.1 | 1943.0 | GPT-5.5 |
| LMArena Text: coding | 1427 | 1494 | GPT-5.5 |
| LMArena Text: software and IT | 1435 | 1493 | GPT-5.5 |
| LMArena WebDev: React | Not measured | 1506 | Only GPT-5.5 measured |
| LMArena WebDev: HTML | Not measured | 1543 | Only GPT-5.5 measured |
| LMArena WebDev: from an image | Not measured | 1532 | Only GPT-5.5 measured |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 38.4 | GPT-5.5 |
| Epoch Capabilities Index | Not measured | 159.7 | Only GPT-5.5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 36.4 | Only GPT-5.5 measured |
| Design Arena: agents building web apps | Not measured | 1117 | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | Not measured | 1089 | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | Not measured | 1143 | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | Not measured | 1169 | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | Not measured | 1173 | Only GPT-5.5 measured |
| Design Arena: agents building games | Not measured | 1179 | Only GPT-5.5 measured |
| Design Arena: HTML slides | Not measured | 1067 | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | Not measured | 1108 | Only GPT-5.5 measured |
| LiveBench research | Not measured | 61.6% | Only GPT-5.5 measured |
| LiveBench browser | Not measured | 48.0% | Only GPT-5.5 measured |
| LiveBench long agentic | Not measured | 44.6% | Only GPT-5.5 measured |
| SREBench (Vals AI) | Not measured | 3.8% | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | Not measured | 51.8% | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 40.4% | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 64.5% | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | Not measured | 8.3% | Only GPT-5.5 measured |
| τ²-Bench Telecom | 36.5% | 93.9% | GPT-5.5 |
| APEX-Agents | Not measured | 55.1% | Only GPT-5.5 measured |
| METR time horizons | 53.8% | Not measured | Only R1 0528 measured |
| Remote Labor Index | Not measured | 6.3% | Only GPT-5.5 measured |
| OSWorld 2.0 | Not measured | 13.0% | Only GPT-5.5 measured |
| DeepResearch Bench | 35.1% | 54.0% | GPT-5.5 |
| PostTrainBench | Not measured | 27.2% | Only GPT-5.5 measured |
| Vending-Bench 2 | Not measured | $7,524 | Only GPT-5.5 measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 93.2% | Only GPT-5.5 measured |
| LiveBench reasoning | Not measured | 89.7% | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | Not measured | 88.3% | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | 93.5% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 45.8% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | 76.3% | 90.7% | GPT-5.5 |
| ARC-AGI | 21.2% | 95.0% | GPT-5.5 |
| ARC-AGI-2 | 1.1% | 85.0% | GPT-5.5 |
| SimpleBench | 40.8% | 69.0% | GPT-5.5 |
| Chess puzzles | Not measured | 63.0% | Only GPT-5.5 measured |
| Mystery game puzzles | Not measured | 56.0% | Only GPT-5.5 measured |
| EBR-bench | Not measured | 34.3% | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1415 | 1489 | GPT-5.5 |
| LMArena Text: expert | 1394 | 1507 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1422 | 1483 | GPT-5.5 |
| LMArena Vision: diagrams | Not measured | 1325 | Only GPT-5.5 measured |
| LMArena Vision: homework | Not measured | 1332 | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | R1 0528 | GPT-5.5 | 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% | 84.4% | GPT-5.5 |
| FrontierMath, tiers 1 to 3 | Not measured | 85.3% | Only GPT-5.5 measured |
| FrontierMath, tier 4 | Not measured | 72.5% | Only GPT-5.5 measured |
| ProofBench | Not measured | 50.0% | Only GPT-5.5 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | 1487 | GPT-5.5 |
| LMArena Text: mathematical work | 1415 | 1496 | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.1% | Only GPT-5.5 measured |
| LegalBench (Vals AI) | Not measured | 86.5% | Only GPT-5.5 measured |
| CaseLaw (Vals AI) | Not measured | 66.2% | Only GPT-5.5 measured |
| CorpFin (Vals AI) | Not measured | 68.4% | Only GPT-5.5 measured |
| TaxEval (Vals AI) | Not measured | 75.0% | Only GPT-5.5 measured |
| MortgageTax (Vals AI) | Not measured | 68.8% | Only GPT-5.5 measured |
| MedCode (Vals AI) | Not measured | 49.1% | Only GPT-5.5 measured |
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| SimpleQA Verified | Not measured | 63.0% | Only GPT-5.5 measured |
| LMArena Text: science | 1430 | 1485 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1445 | 1465 | GPT-5.5 |
| LMArena Text: legal and government | 1419 | 1491 | GPT-5.5 |
| LMArena Text: business and finance | 1402 | 1478 | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | 1256 | GPT-5.5 |
| Design Arena: UI components | 1102 | 1260 | GPT-5.5 |
| Design Arena: game dev | 1098 | 1294 | GPT-5.5 |
| Design Arena: data viz | 1182 | 1265 | GPT-5.5 |
| Design Arena: 3D | 1111 | 1198 | GPT-5.5 |
| Design Arena: all code categories | 1139 | 1260 | GPT-5.5 |
| Design Arena: SVG | 1038 | 1234 | GPT-5.5 |
| Design Arena: ASCII art | Not measured | 1254 | Only GPT-5.5 measured |
| LiveBench design | Not measured | 70.3% | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | R1 0528 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.6% | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | Not measured | 60.9% | Only GPT-5.5 measured |
| SAGE (Vals AI) | Not measured | 51.5% | Only GPT-5.5 measured |
| MedScribe (Vals AI) | Not measured | 86.9% | Only GPT-5.5 measured |
| Vals Multimodal Index | Not measured | 68.1% | Only GPT-5.5 measured |
| IFBench | 39.6% | 75.9% | GPT-5.5 |
| Long context reasoning (AA-LCR) | 55.7% | 84.3% | GPT-5.5 |
| DTBench | Not measured | 96.0% | Only GPT-5.5 measured |
| LMCA | Not measured | 54.3% | Only GPT-5.5 measured |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| Surface Evolver Bench | Not measured | 88.1% | Only GPT-5.5 measured |
| Furniture assembly | Not measured | 44.2% | Only GPT-5.5 measured |
| ExploitBench | Not measured | 47.4% | Only GPT-5.5 measured |
| CL-bench Life | Not measured | 22.2% | Only GPT-5.5 measured |
| LMArena Text | 1428 | 1471 | GPT-5.5 |
| LMArena Text: instruction following | 1382 | 1478 | GPT-5.5 |
| LMArena Text: creative writing | 1405 | 1455 | GPT-5.5 |
| LMArena Vision | Not measured | 1297 | Only GPT-5.5 measured |
| LMArena Text: English | 1437 | 1470 | GPT-5.5 |
| LMArena Text: non-English | 1412 | 1467 | GPT-5.5 |
| LMArena Text: Chinese | 1442 | 1534 | GPT-5.5 |
| LMArena Text: German | 1404 | 1480 | GPT-5.5 |
| LMArena Text: Japanese | 1391 | 1499 | GPT-5.5 |
| LMArena Text: Korean | 1360 | 1460 | GPT-5.5 |
| LMArena Text: French | 1417 | 1485 | GPT-5.5 |
| LMArena Text: Spanish | 1411 | 1468 | GPT-5.5 |
| LMArena Text: Russian | 1423 | 1473 | GPT-5.5 |
| LMArena Text: Polish | 1428 | 1465 | GPT-5.5 |
| LMArena Text: multi-turn | 1405 | 1476 | GPT-5.5 |
| LMArena Text: longer queries | 1391 | 1484 | GPT-5.5 |
| LMArena Text: writing and language | 1402 | 1466 | GPT-5.5 |
| LMArena Text: entertainment and media | 1400 | 1449 | GPT-5.5 |
| LMArena Vision: English | Not measured | 1292 | Only GPT-5.5 measured |
| LMArena Vision: Chinese | Not measured | 1360 | Only GPT-5.5 measured |
| LMArena Vision: reading text | Not measured | 1306 | Only GPT-5.5 measured |
| LMArena Vision: humor | Not measured | 1293 | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | Not measured | 1231 | Only GPT-5.5 measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-5.5 measured |
| LMArena Document | Not measured | 1486 | Only GPT-5.5 measured |
| Neither model has a result on the other 6 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | GPT-5.5 | 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
R1 0528 costs less
Repository review
50K input + 3K output tokens
R1 0528 costs less
Agent loop
200K input + 10K output tokens
R1 0528 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 | OpenAI GPT-5.5 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | May 28, 2025 | April 24, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.50 | $5.00 |
| Output $/Mtok | $2.15 | $30.00 |
| Coding index | 71.4 | 67.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $1.65 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is R1 0528 better than GPT-5.5 for coding?
Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 67.8 for GPT-5.5, though on 1 benchmark against 2. Pick GPT-5.5 when the context window is larger, 1.05M against 164K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.
Which is cheaper, R1 0528 or GPT-5.5?
R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; GPT-5.5 costs $5.00 and $30.00. Cost per solved task is known for GPT-5.5 only.
Which has the bigger context window, R1 0528 or GPT-5.5?
GPT-5.5, with 1.05M tokens against 164K.
How many benchmarks is the R1 0528 vs GPT-5.5 comparison based on?
50 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and GPT-5.5's on 2. Where only one model has a result, the page says so instead of naming a winner.