Gemma 4 31B vs GPT-3.5 Turbo (older v0613)
Gemma 4 31B costs 5.0x less per output token than GPT-3.5 Turbo (older v0613) ($0.40 vs $2.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- 39.0
- Overall
- 42.1
- Output $/M
- $0.40
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
Gemma 4 31B or GPT-3.5 Turbo (older v0613): which to pick
Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; GPT-3.5 Turbo (older v0613) has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Gemma 4 31B has results on LiveBench coding, LMArena WebDev and AA Coding Index; GPT-3.5 Turbo (older v0613) on GSM8K, MMLU and WinoGrande. 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
Only Gemma 4 31B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Gemma 4 31B has a AA Intelligence Index result, 14.7.
Directional only
Agentic work
AA Agentic Index
No call
Only Gemma 4 31B has a AA Agentic Index result, 4.2.
Directional only
Long documents
Listed context window
Gemma 4 31B
Gemma 4 31B takes 262K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 4 31B
Gemma 4 31B $0.0003, GPT-3.5 Turbo (older v0613) $0.0020 per turn.
List prices
At a glance
- Coding index
- Gemma 4 31B 39.0 from 2 benchmarks, GPT-3.5 Turbo (older v0613) 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 4 31B $0.40 against GPT-3.5 Turbo (older v0613) $2.00 per million output tokens, 5.0x apart.
- Context window
- Gemma 4 31B takes 262K tokens, GPT-3.5 Turbo (older v0613) 4K.
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 Index12 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| LiveBench coding | 49.0% | Not measured | Only Gemma 4 31B measured |
| LMArena WebDev | 1368 | Not measured | Only Gemma 4 31B measured |
| AA Coding Index | 43.4 | Not measured | Only Gemma 4 31B measured |
| SciCode | 45.5% | Not measured | Only Gemma 4 31B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 36.4% | Not measured | Only Gemma 4 31B measured |
| WeirdML | 52.3% | Not measured | Only Gemma 4 31B measured |
| ALE-Bench | 925.5 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: coding | 1459 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: software and IT | 1463 | Not measured | Only Gemma 4 31B measured |
| LMArena WebDev: React | 1360 | Not measured | Only Gemma 4 31B measured |
| LMArena WebDev: HTML | 1381 | Not measured | Only Gemma 4 31B measured |
| SciCode | 8.9% | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.7 | Not measured | Only Gemma 4 31B measured |
| Epoch Capabilities Index | 143.0 | Not measured | Only Gemma 4 31B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Agentic Index | 4.2 | Not measured | Only Gemma 4 31B measured |
| LiveBench research | 44.4% | Not measured | Only Gemma 4 31B measured |
| LiveBench browser | 4.0% | Not measured | Only Gemma 4 31B measured |
| LiveBench long agentic | 13.3% | Not measured | Only Gemma 4 31B measured |
| τ²-Bench Telecom | 65.5% | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| LiveBench reasoning | 68.3% | Not measured | Only Gemma 4 31B measured |
| GPQA Diamond (Artificial Analysis run) | 85.7% | Not measured | Only Gemma 4 31B measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.6% | Not measured | Only Gemma 4 31B measured |
| GPQA Diamond (Epoch AI run) | 75.8% | Not measured | Only Gemma 4 31B measured |
| LMArena Text: hard prompts | 1448 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: expert | 1465 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: hard prompts in English | 1457 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: diagrams | 1283 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: homework | 1310 | Not measured | Only Gemma 4 31B measured |
| Kagi LLM Benchmark | 63.5% | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| OTIS Mock AIME | 73.3% | Not measured | Only Gemma 4 31B measured |
| GSM8K | Not measured | 46.9% | Only GPT-3.5 Turbo (older v0613) measured |
| LMArena Text: math | 1464 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: mathematical work | 1465 | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 52.6% | Not measured | Only Gemma 4 31B measured |
| MortgageTax (Vals AI) | 61.4% | Not measured | Only Gemma 4 31B measured |
| SimpleQA Verified | 10.4% | Not measured | Only Gemma 4 31B measured |
| MMLU | Not measured | 68.9% | Only GPT-3.5 Turbo (older v0613) measured |
| LMArena Text: science | 1457 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: medicine and healthcare | 1451 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: legal and government | 1438 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: business and finance | 1430 | Not measured | Only Gemma 4 31B measured |
| FACTS Benchmark Suite | 45.3% | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| LiveBench design | 58.4% | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index32 boards measured
| Benchmark | Gemma 4 31B | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| LiveBench data analysis | 53.3% | Not measured | Only Gemma 4 31B measured |
| SAGE (Vals AI) | 55.0% | Not measured | Only Gemma 4 31B measured |
| IFBench | 75.6% | Not measured | Only Gemma 4 31B measured |
| Long context reasoning (AA-LCR) | 69.7% | Not measured | Only Gemma 4 31B measured |
| DTBench | 82.7% | Not measured | Only Gemma 4 31B measured |
| LMCA | 39.3% | Not measured | Only Gemma 4 31B measured |
| Surface Evolver Bench | 30.6% | Not measured | Only Gemma 4 31B measured |
| WinoGrande | Not measured | 81.6% | Only GPT-3.5 Turbo (older v0613) measured |
| LMArena Text | 1443 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: instruction following | 1434 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: creative writing | 1416 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision | 1276 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: English | 1451 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: non-English | 1430 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: Chinese | 1475 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: French | 1436 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: Spanish | 1442 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: Russian | 1461 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: Polish | 1431 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: multi-turn | 1452 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: longer queries | 1446 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: writing and language | 1431 | Not measured | Only Gemma 4 31B measured |
| LMArena Text: entertainment and media | 1405 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: English | 1271 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: Chinese | 1326 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: reading text | 1282 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: humor | 1280 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: captioning | 1218 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: recognizing things | 1226 | Not measured | Only Gemma 4 31B measured |
| LMArena Vision: creative writing | 1285 | Not measured | Only Gemma 4 31B measured |
| LMArena Document | 1425 | Not measured | Only Gemma 4 31B measured |
| EQ-Bench 4 | 1120 | Not measured | Only Gemma 4 31B measured |
| Neither model has a result on the other 16 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 4 31B costs less
Repository review
50K input + 3K output tokens
Gemma 4 31B costs less
Agent loop
200K input + 10K output tokens
Gemma 4 31B 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 4 31B | OpenAI GPT-3.5 Turbo (older v0613) |
|---|---|---|
| Maker | OpenAI | |
| Weights | Open weights | No public weights listed |
| Released | April 2, 2026 | January 25, 2024 |
| Context window | 262K | 4K |
| Input $/Mtok | $0.14 | $1.00 |
| Output $/Mtok | $0.40 | $2.00 |
| Coding index | 39.0 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemma 4 31B better than GPT-3.5 Turbo (older v0613) for coding?
Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; GPT-3.5 Turbo (older v0613) has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Gemma 4 31B or GPT-3.5 Turbo (older v0613)?
Gemma 4 31B costs $0.14 per million input tokens and $0.40 per million output tokens; GPT-3.5 Turbo (older v0613) costs $1.00 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 4 31B or GPT-3.5 Turbo (older v0613)?
Gemma 4 31B, with 262K tokens against 4K.
Has any benchmark tested both Gemma 4 31B and GPT-3.5 Turbo (older v0613)?
No benchmark has tested both yet. Gemma 4 31B has results on LiveBench coding, LMArena WebDev and AA Coding Index; GPT-3.5 Turbo (older v0613) on GSM8K, MMLU and WinoGrande. Compare them on price and context instead. Data as of October 23, 2026.