DeepSeek V3.1 vs Gemma 3 4B
DeepSeek V3.1 leads Gemma 3 4B on the Artificial Analysis Intelligence Index, 13.7 to 4.8, and Gemma 3 4B costs 20.0x less per output token ($0.075 vs $1.50 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
DeepSeek V3.1 or Gemma 3 4B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for a larger context window (164K against 131K tokens).
- Shared results
- 4
- DeepSeek V3.1 only
- 7
- Gemma 3 4B only
- 38
- 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
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 4.8.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 has a Design Arena: websites result, 1123.
Directional only
Long documents
Listed context window
DeepSeek V3.1
DeepSeek V3.1 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 4B
DeepSeek V3.1 $0.0013, Gemma 3 4B $0.0001 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Gemma 3 4B not benchmarked.
- Widest category
- Reasoning, 28.0 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: DeepSeek V3.1 82.7% against Gemma 3 4B 50.9%, 31.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Gemma 3 4B $0.075 per million output tokens, 20.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Gemma 3 4B 131K.
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 V3.1 | Bars | Gemma 3 4B | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 2.7 | Only Gemma 3 4B measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 11.2% | Only Gemma 3 4B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 0.8% | Only Gemma 3 4B measured |
| LMArena Text: coding | Not measured | 1230 | Only Gemma 3 4B measured |
| LMArena Text: software and IT | Not measured | 1269 | Only Gemma 3 4B measured |
| SciCode | Not measured | 0.0% | Only Gemma 3 4B measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 4.8 | DeepSeek V3.1 |
| Epoch Capabilities Index | Not measured | 115.9 | Only Gemma 3 4B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 5.0% | Only Gemma 3 4B measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 19.6% | Only Gemma 3 4B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 29.1% | Only Gemma 3 4B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 5.3% | Only Gemma 3 4B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 23.2% | Only Gemma 3 4B measured |
| LMArena Text: hard prompts | Not measured | 1253 | Only Gemma 3 4B measured |
| LMArena Text: expert | Not measured | 1223 | Only Gemma 3 4B measured |
| LMArena Text: hard prompts in English | Not measured | 1253 | Only Gemma 3 4B measured |
| Kagi LLM Benchmark | 53.2% | 25.2% | DeepSeek V3.1 |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 12.7 | Only Gemma 3 4B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 12.7% | Only Gemma 3 4B measured |
| OTIS Mock AIME | Not measured | 7.5% | Only Gemma 3 4B measured |
| LMArena Text: math | Not measured | 1239 | Only Gemma 3 4B measured |
| LMArena Text: mathematical work | Not measured | 1256 | Only Gemma 3 4B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 41.7% | Only Gemma 3 4B measured |
| LMArena Text: science | Not measured | 1306 | Only Gemma 3 4B measured |
| LMArena Text: medicine and healthcare | Not measured | 1342 | Only Gemma 3 4B measured |
| LMArena Text: legal and government | Not measured | 1327 | Only Gemma 3 4B measured |
| LMArena Text: business and finance | Not measured | 1311 | Only Gemma 3 4B 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 | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1085 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1095 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | Leader |
|---|---|---|---|
| IFBench | Not measured | 28.3% | Only Gemma 3 4B measured |
| Long context reasoning (AA-LCR) | Not measured | 6.7% | Only Gemma 3 4B measured |
| DTBench | 82.7% | 50.9% | DeepSeek V3.1 |
| LMCA | 24.3% | 2.8% | DeepSeek V3.1 |
| LMArena Text | Not measured | 1291 | Only Gemma 3 4B measured |
| LMArena Text: instruction following | Not measured | 1239 | Only Gemma 3 4B measured |
| LMArena Text: creative writing | Not measured | 1270 | Only Gemma 3 4B measured |
| LMArena Text: English | Not measured | 1300 | Only Gemma 3 4B measured |
| LMArena Text: non-English | Not measured | 1273 | Only Gemma 3 4B measured |
| LMArena Text: German | Not measured | 1281 | Only Gemma 3 4B measured |
| LMArena Text: Russian | Not measured | 1294 | Only Gemma 3 4B measured |
| LMArena Text: multi-turn | Not measured | 1255 | Only Gemma 3 4B measured |
| LMArena Text: longer queries | Not measured | 1273 | Only Gemma 3 4B measured |
| LMArena Text: writing and language | Not measured | 1288 | Only Gemma 3 4B measured |
| LMArena Text: entertainment and media | Not measured | 1233 | Only Gemma 3 4B measured |
| Neither model has a result on the other 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Gemma 3 4B | 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 3 4B costs less
Repository review
50K input + 3K output tokens
Gemma 3 4B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 4B 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 V3.1 | Google Gemma 3 4B |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | March 13, 2025 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.55 | $0.045 |
| Output $/Mtok | $1.50 | $0.075 |
| 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 V3.1 better than Gemma 3 4B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for a larger context window (164K against 131K tokens).
Which is cheaper, DeepSeek V3.1 or Gemma 3 4B?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Gemma 3 4B costs $0.045 and $0.075. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Gemma 3 4B?
DeepSeek V3.1, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.1 vs Gemma 3 4B comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Gemma 3 4B's on 0. Where only one model has a result, the page says so instead of naming a winner.