R1 0528 vs GPT-5.1-Codex-Max
R1 0528 costs 4.6x less per output token than GPT-5.1-Codex-Max ($2.18 vs $10.00 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
R1 0528 or GPT-5.1-Codex-Max: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick R1 0528 for output tokens 4.6x cheaper ($2.18 vs $10.00 per million) and work measured by ALE-Bench or METR time horizons, where it leads.
- Shared results
- 2
- R1 0528 only
- 58
- GPT-5.1-Codex-Max only
- 4
- 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
No call
Only R1 0528 has a AA Intelligence Index result, 13.1.
Directional only
Web design
Design Arena: websites
No call
Only R1 0528 has a Design Arena: websites result, 1149.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
R1 0528
R1 0528 $0.0016, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, GPT-5.1-Codex-Max not benchmarked.
- Widest category
- Agentic work, 17.0 points apart on average over 1 shared benchmark.
- Widest single gap
- METR time horizons: R1 0528 53.8% against GPT-5.1-Codex-Max 70.8%, 17.0 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against GPT-5.1-Codex-Max $10.00 per million output tokens, 4.6x apart.
- Context window
- R1 0528 takes 164K tokens, GPT-5.1-Codex-Max 400K.
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-5.1-Codex-Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | R1 0528 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveCodeBench (Vals AI) | Not measured | 83.6% | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 22.2% | Only GPT-5.1-Codex-Max 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 |
| Terminal-Bench | Not measured | 60.4% | Only GPT-5.1-Codex-Max measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | 1228.3 | GPT-5.1-Codex-Max |
| 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 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | R1 0528 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | Not measured | Only R1 0528 measured |
| METR time horizons | 53.8% | 70.8% | GPT-5.1-Codex-Max |
| 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 | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | R1 0528 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only R1 0528 measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | Not measured | Only R1 0528 measured |
| 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 index7 boards, none shared
| Benchmark | R1 0528 | GPT-5.1-Codex-Max | 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 |
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max 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 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | R1 0528 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| 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 | GPT-5.1-Codex-Max | 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 | GPT-5.1-Codex-Max | 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 | GPT-5.1-Codex-Max | 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-5.1-Codex-Max |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | May 28, 2025 | December 4, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.50 | $1.25 |
| Output $/Mtok | $2.18 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is R1 0528 better than GPT-5.1-Codex-Max for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 4.6x cheaper ($2.18 vs $10.00 per million) and work measured by ALE-Bench or METR time horizons, where it leads.
Which is cheaper, R1 0528 or GPT-5.1-Codex-Max?
R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; GPT-5.1-Codex-Max costs $1.25 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, R1 0528 or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max, with 400K tokens against 164K.
How many benchmarks is the R1 0528 vs GPT-5.1-Codex-Max comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 0 independent benchmarks and GPT-5.1-Codex-Max's on 1. Where only one model has a result, the page says so instead of naming a winner.