R1 0528 vs Gemma 2 27B
Gemma 2 27B costs 3.4x less per output token than R1 0528 ($0.65 vs $2.18 per million). Updated October 22, 2026; 31 benchmarks have a result for both.
R1 0528 or Gemma 2 27B: 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 8K tokens).
- Shared results
- 31
- R1 0528 only
- 29
- Gemma 2 27B only
- 5
- Like-for-like categories
- 5 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
R1 0528
R1 0528 takes 164K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 2 27B
R1 0528 $0.0016, Gemma 2 27B $0.0010 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, Gemma 2 27B not benchmarked.
- Widest category
- Math, 49.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- MATH level 5: R1 0528 96.6% against Gemma 2 27B 27.9%, 68.8 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against Gemma 2 27B $0.65 per million output tokens, 3.4x apart.
- Context window
- R1 0528 takes 164K tokens, Gemma 2 27B 8K.
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 | Gemma 2 27B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 31 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | R1 0528 | Gemma 2 27B | 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 | 1211 | R1 0528 |
| LMArena Text: software and IT | 1435 | 1228 | R1 0528 |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | R1 0528 | Gemma 2 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | Not measured | Only R1 0528 measured |
| Epoch Capabilities Index | Not measured | 122.1 | Only Gemma 2 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | R1 0528 | Gemma 2 27B | 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 index40
| Benchmark | R1 0528 | Gemma 2 27B | 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% | 36.5% | R1 0528 |
| 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 | 1198 | R1 0528 |
| LMArena Text: expert | 1394 | 1171 | R1 0528 |
| LMArena Text: hard prompts in English | 1422 | 1206 | R1 0528 |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | R1 0528 | Gemma 2 27B | 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% | 1.4% | R1 0528 |
| MATH level 5 | 96.6% | 27.9% | R1 0528 |
| LMArena Text: math | 1400 | 1212 | R1 0528 |
| LMArena Text: mathematical work | 1415 | 1221 | R1 0528 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | R1 0528 | Gemma 2 27B | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 68.0% | Only Gemma 2 27B measured |
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| MMLU | Not measured | 75.7% | Only Gemma 2 27B measured |
| LMArena Text: science | 1430 | 1228 | R1 0528 |
| LMArena Text: medicine and healthcare | 1445 | 1198 | R1 0528 |
| LMArena Text: legal and government | 1419 | 1241 | R1 0528 |
| LMArena Text: business and finance | 1402 | 1208 | R1 0528 |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | R1 0528 | Gemma 2 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | Not measured | Only R1 0528 measured |
| Design Arena: UI components | 1100 | 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 | 1110 | Not measured | Only R1 0528 measured |
| Design Arena: all code categories | 1138 | Not measured | Only R1 0528 measured |
| Design Arena: SVG | 1038 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | R1 0528 | Gemma 2 27B | Leader |
|---|---|---|---|
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | Not measured | Only R1 0528 measured |
| DTBench | Not measured | 48.0% | Only Gemma 2 27B measured |
| LMCA | Not measured | 7.1% | Only Gemma 2 27B 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 | 1231 | R1 0528 |
| LMArena Text: instruction following | 1382 | 1206 | R1 0528 |
| LMArena Text: creative writing | 1405 | 1240 | R1 0528 |
| LMArena Text: English | 1437 | 1242 | R1 0528 |
| LMArena Text: non-English | 1412 | 1217 | R1 0528 |
| LMArena Text: Chinese | 1442 | 1221 | R1 0528 |
| LMArena Text: German | 1404 | 1209 | R1 0528 |
| LMArena Text: Japanese | 1391 | 1175 | R1 0528 |
| LMArena Text: Korean | 1360 | 1174 | R1 0528 |
| LMArena Text: French | 1417 | 1248 | R1 0528 |
| LMArena Text: Spanish | 1411 | 1228 | R1 0528 |
| LMArena Text: Russian | 1423 | 1234 | R1 0528 |
| LMArena Text: Polish | 1428 | 1277 | R1 0528 |
| LMArena Text: multi-turn | 1405 | 1224 | R1 0528 |
| LMArena Text: longer queries | 1391 | 1230 | R1 0528 |
| LMArena Text: writing and language | 1402 | 1249 | R1 0528 |
| LMArena Text: entertainment and media | 1400 | 1212 | R1 0528 |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | Gemma 2 27B | 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
Gemma 2 27B 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 | Google Gemma 2 27B |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | Open weights |
| Released | May 28, 2025 | July 13, 2024 |
| Context window | 164K | 8K |
| Input $/Mtok | $0.50 | $0.65 |
| Output $/Mtok | $2.18 | $0.65 |
| 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 Gemma 2 27B 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 8K tokens).
Which is cheaper, R1 0528 or Gemma 2 27B?
R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Gemma 2 27B costs $0.65 and $0.65. Cost per solved task is known for neither of them.
Which has the bigger context window, R1 0528 or Gemma 2 27B?
R1 0528, with 164K tokens against 8K.
How many benchmarks is the R1 0528 vs Gemma 2 27B comparison based on?
31 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and Gemma 2 27B's on 0. Where only one model has a result, the page says so instead of naming a winner.