DeepSeek V3 0324 vs Gemma 2 27B
Gemma 2 27B costs 1.6x less per output token than DeepSeek V3 0324 ($0.65 vs $1.07 per million). Updated October 22, 2026; 6 benchmarks have a result for both.
DeepSeek V3 0324 or Gemma 2 27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3 0324 for a larger context window (164K against 8K tokens).
- Shared results
- 6
- DeepSeek V3 0324 only
- 29
- Gemma 2 27B only
- 30
- Like-for-like categories
- 4 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 DeepSeek V3 0324 has a AA Intelligence Index result, 9.7.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V3 0324 has a AA Agentic Index result, 0.8.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V3 0324 has a GPQA Diamond result, 61.6%.
Directional only
Math
AIME
No call
Only DeepSeek V3 0324 has a AIME result, 52.2%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3 0324 has a MMLU Pro result, 79.5%.
Directional only
Long documents
Listed context window
DeepSeek V3 0324
DeepSeek V3 0324 takes 164K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3 0324
DeepSeek V3 0324 $0.0008, Gemma 2 27B $0.0010 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 0324 not benchmarked, Gemma 2 27B not benchmarked.
- Widest category
- Math, 42.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- MATH level 5: DeepSeek V3 0324 75.5% against Gemma 2 27B 27.9%, 47.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.07 against Gemma 2 27B $0.65 per million output tokens, 1.6x apart.
- Context window
- DeepSeek V3 0324 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 | DeepSeek V3 0324 | Bars | Gemma 2 27B | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 6 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | Not measured | Only DeepSeek V3 0324 measured |
| AA Coding Index | 21.2 | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Vals AI) | 65.5% | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Artificial Analysis run) | 40.5% | Not measured | Only DeepSeek V3 0324 measured |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | Not measured | Only DeepSeek V3 0324 measured |
| WeirdML | 36.1% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: coding | Not measured | 1211 | Only Gemma 2 27B measured |
| LMArena Text: software and IT | Not measured | 1228 | Only Gemma 2 27B measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | Not measured | Only DeepSeek V3 0324 measured |
| Epoch Capabilities Index | Not measured | 122.1 | Only Gemma 2 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| τ²-Bench Telecom | 47.1% | Not measured | Only DeepSeek V3 0324 measured |
| METR time horizons | 49.6% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | Not measured | Only DeepSeek V3 0324 measured |
| GPQA Diamond (Artificial Analysis run) | 65.5% | Not measured | Only DeepSeek V3 0324 measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | Not measured | Only DeepSeek V3 0324 measured |
| GPQA Diamond (Epoch AI run) | 67.6% | 36.5% | DeepSeek V3 0324 |
| SimpleBench | 27.2% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: hard prompts | Not measured | 1198 | Only Gemma 2 27B measured |
| LMArena Text: expert | Not measured | 1171 | Only Gemma 2 27B measured |
| LMArena Text: hard prompts in English | Not measured | 1206 | Only Gemma 2 27B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| AIME | 52.2% | Not measured | Only DeepSeek V3 0324 measured |
| MATH 500 (Vals AI) | 88.6% | Not measured | Only DeepSeek V3 0324 measured |
| MGSM (Vals AI) | 91.7% | Not measured | Only DeepSeek V3 0324 measured |
| AA Math Index | 41.0 | Not measured | Only DeepSeek V3 0324 measured |
| AIME 2025 (Artificial Analysis run) | 41.0% | Not measured | Only DeepSeek V3 0324 measured |
| OTIS Mock AIME | 37.8% | 1.4% | DeepSeek V3 0324 |
| MATH level 5 | 75.5% | 27.9% | DeepSeek V3 0324 |
| LMArena Text: math | Not measured | 1212 | Only Gemma 2 27B measured |
| LMArena Text: mathematical work | Not measured | 1221 | Only Gemma 2 27B measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | Not measured | Only DeepSeek V3 0324 measured |
| LegalBench (Vals AI) | 77.7% | 68.0% | DeepSeek V3 0324 |
| CorpFin (Vals AI) | 54.7% | Not measured | Only DeepSeek V3 0324 measured |
| TaxEval (Vals AI) | 71.1% | Not measured | Only DeepSeek V3 0324 measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only DeepSeek V3 0324 measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | Not measured | Only DeepSeek V3 0324 measured |
| MMLU | Not measured | 75.7% | Only Gemma 2 27B measured |
| LMArena Text: science | Not measured | 1228 | Only Gemma 2 27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1198 | Only Gemma 2 27B measured |
| LMArena Text: legal and government | Not measured | 1241 | Only Gemma 2 27B measured |
| LMArena Text: business and finance | Not measured | 1208 | Only Gemma 2 27B measured |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3 0324 | Gemma 2 27B | Leader |
|---|---|---|---|
| IFBench | 41.0% | Not measured | Only DeepSeek V3 0324 measured |
| Long context reasoning (AA-LCR) | 40.7% | Not measured | Only DeepSeek V3 0324 measured |
| DTBench | 64.8% | 48.0% | DeepSeek V3 0324 |
| LMCA | 15.5% | 7.1% | DeepSeek V3 0324 |
| Fiction.LiveBench | 50.0% | Not measured | Only DeepSeek V3 0324 measured |
| Creative writing (Lech Mazur) | 77.0% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text | Not measured | 1231 | Only Gemma 2 27B measured |
| LMArena Text: instruction following | Not measured | 1206 | Only Gemma 2 27B measured |
| LMArena Text: creative writing | Not measured | 1240 | Only Gemma 2 27B measured |
| LMArena Text: English | Not measured | 1242 | Only Gemma 2 27B measured |
| LMArena Text: non-English | Not measured | 1217 | Only Gemma 2 27B measured |
| LMArena Text: Chinese | Not measured | 1221 | Only Gemma 2 27B measured |
| LMArena Text: German | Not measured | 1209 | Only Gemma 2 27B measured |
| LMArena Text: Japanese | Not measured | 1175 | Only Gemma 2 27B measured |
| LMArena Text: Korean | Not measured | 1174 | Only Gemma 2 27B measured |
| LMArena Text: French | Not measured | 1248 | Only Gemma 2 27B measured |
| LMArena Text: Spanish | Not measured | 1228 | Only Gemma 2 27B measured |
| LMArena Text: Russian | Not measured | 1234 | Only Gemma 2 27B measured |
| LMArena Text: Polish | Not measured | 1277 | Only Gemma 2 27B measured |
| LMArena Text: multi-turn | Not measured | 1224 | Only Gemma 2 27B measured |
| LMArena Text: longer queries | Not measured | 1230 | Only Gemma 2 27B measured |
| LMArena Text: writing and language | Not measured | 1249 | Only Gemma 2 27B measured |
| LMArena Text: entertainment and media | Not measured | 1212 | Only Gemma 2 27B measured |
| 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 | DeepSeek V3 0324 | 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
DeepSeek V3 0324 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3 0324 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3 0324 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 V3 0324 | Google Gemma 2 27B |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | Open weights |
| Released | March 24, 2025 | July 13, 2024 |
| Context window | 164K | 8K |
| Input $/Mtok | $0.27 | $0.65 |
| Output $/Mtok | $1.07 | $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 DeepSeek V3 0324 better than Gemma 2 27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3 0324 for a larger context window (164K against 8K tokens).
Which is cheaper, DeepSeek V3 0324 or Gemma 2 27B?
DeepSeek V3 0324 costs $0.27 per million input tokens and $1.07 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, DeepSeek V3 0324 or Gemma 2 27B?
DeepSeek V3 0324, with 164K tokens against 8K.
How many benchmarks is the DeepSeek V3 0324 vs Gemma 2 27B comparison based on?
6 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3 0324'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.