Gemma 3 4B vs GPT-5.1-Codex-Max
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- 4.8
- Output $/M
- $0.075
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Gemma 3 4B has the edge where output tokens cost 133.3x less.
No benchmark has tested both yet. Gemma 3 4B has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. Compare them on price and context instead.
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 Gemma 3 4B has a AA Intelligence Index result, 4.8.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 4B
Gemma 3 4B $0.0001, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- Gemma 3 4B not benchmarked, GPT-5.1-Codex-Max not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Gemma 3 4B $0.075 against GPT-5.1-Codex-Max $10.00 per million output tokens, 133.3x apart.
- Context window
- Gemma 3 4B takes 131K tokens, GPT-5.1-Codex-Max 400K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| AA Coding Index | 2.7 | Not measured | Only Gemma 3 4B 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) | 11.2% | Not measured | Only Gemma 3 4B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | Not measured | Only Gemma 3 4B measured |
| Terminal-Bench | Not measured | 60.4% | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | Not measured | 1228.3 | Only GPT-5.1-Codex-Max measured |
| LMArena Text: coding | 1230 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: software and IT | 1269 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | Not measured | Only Gemma 3 4B measured |
| Epoch Capabilities Index | 115.9 | Not measured | Only Gemma 3 4B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 5.0% | Not measured | Only Gemma 3 4B measured |
| METR time horizons | Not measured | 70.8% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 29.1% | Not measured | Only Gemma 3 4B measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | Not measured | Only Gemma 3 4B measured |
| GPQA Diamond (Epoch AI run) | 23.2% | Not measured | Only Gemma 3 4B measured |
| LMArena Text: hard prompts | 1253 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: expert | 1223 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: hard prompts in English | 1253 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| AA Math Index | 12.7 | Not measured | Only Gemma 3 4B measured |
| AIME 2025 (Artificial Analysis run) | 12.7% | Not measured | Only Gemma 3 4B measured |
| OTIS Mock AIME | 7.5% | Not measured | Only Gemma 3 4B measured |
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max measured |
| LMArena Text: math | 1239 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: mathematical work | 1256 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 41.7% | Not measured | Only Gemma 3 4B measured |
| LMArena Text: science | 1306 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: medicine and healthcare | 1342 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: legal and government | 1327 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: business and finance | 1311 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 12 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index15 boards measured
| Benchmark | Gemma 3 4B | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| IFBench | 28.3% | Not measured | Only Gemma 3 4B measured |
| Long context reasoning (AA-LCR) | 6.7% | Not measured | Only Gemma 3 4B measured |
| DTBench | 50.9% | Not measured | Only Gemma 3 4B measured |
| LMCA | 2.8% | Not measured | Only Gemma 3 4B measured |
| LMArena Text | 1291 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: instruction following | 1239 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: creative writing | 1270 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: English | 1300 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: non-English | 1273 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: German | 1281 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: Russian | 1294 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: multi-turn | 1255 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: longer queries | 1273 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: writing and language | 1288 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: entertainment and media | 1233 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 31 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 | Google Gemma 3 4B | OpenAI GPT-5.1-Codex-Max |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | December 4, 2025 |
| Context window | 131K | 400K |
| Input $/Mtok | $0.045 | $1.25 |
| Output $/Mtok | $0.075 | $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 Gemma 3 4B better than GPT-5.1-Codex-Max for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Gemma 3 4B has the edge where output tokens cost 133.3x less.
Which is cheaper, Gemma 3 4B or GPT-5.1-Codex-Max?
Gemma 3 4B costs $0.045 per million input tokens and $0.075 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, Gemma 3 4B or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max, with 400K tokens against 131K.
Has any benchmark tested both Gemma 3 4B and GPT-5.1-Codex-Max?
No benchmark has tested both yet. Gemma 3 4B has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. Compare them on price and context instead. Data as of October 21, 2026.