Gemini 3.1 Flash Lite vs GPT-4
Updated October 21, 2026. 5 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Gemini 3.1 Flash Lite has the edge where output tokens cost 40.0x less, and where the context window is larger, 1.05M against 8K tokens.
- Shared results
- 5
- Gemini 3.1 Flash Lite only
- 10
- GPT-4 only
- 40
- Like-for-like categories
- 3 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 GPT-4 has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Gemini 3.1 Flash Lite
Gemini 3.1 Flash Lite takes 1.05M tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.1 Flash Lite
Gemini 3.1 Flash Lite $0.0010, GPT-4 $0.060 per turn.
List prices
At a glance
- Coding index
- Gemini 3.1 Flash Lite not benchmarked, GPT-4 not benchmarked.
- Widest category
- Reasoning, 47.3 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Gemini 3.1 Flash Lite 82.2% against GPT-4 34.9%, 47.3 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.1 Flash Lite $1.50 against GPT-4 $60.00 per million output tokens, 40.0x apart.
- Context window
- Gemini 3.1 Flash Lite takes 1.05M tokens, GPT-4 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 | Gemini 3.1 Flash Lite | Bars | GPT-4 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 5 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 22.4% | Only GPT-4 measured |
| SWE-bench Lite | Not measured | 39.7% | Only GPT-4 measured |
| SWE-bench (full test set) | Not measured | 12.5% | Only GPT-4 measured |
| AA Coding Index | Not measured | 13.1 | Only GPT-4 measured |
| SciCode | 43.4% | Not measured | Only Gemini 3.1 Flash Lite measured |
| Terminal-Bench Hard (Artificial Analysis run) | 24.2% | Not measured | Only Gemini 3.1 Flash Lite measured |
| WeirdML | 52.2% | Not measured | Only Gemini 3.1 Flash Lite measured |
| LMArena Text: coding | Not measured | 1209 | Only GPT-4 measured |
| LMArena Text: software and IT | Not measured | 1195 | Only GPT-4 measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only GPT-4 measured |
| Epoch Capabilities Index | 144.3 | 125.7 | Gemini 3.1 Flash Lite |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.3% | Not measured | Only Gemini 3.1 Flash Lite measured |
| DeepResearch Bench | 37.3% | Not measured | Only Gemini 3.1 Flash Lite measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 82.2% | 34.9% | Gemini 3.1 Flash Lite |
| Humanity's Last Exam (Artificial Analysis run) | 17.2% | Not measured | Only Gemini 3.1 Flash Lite measured |
| GPQA Diamond (Epoch AI run) | 81.8% | Not measured | Only Gemini 3.1 Flash Lite measured |
| BIG-Bench Hard | Not measured | 75.1% | Only GPT-4 measured |
| LMArena Text: hard prompts | Not measured | 1200 | Only GPT-4 measured |
| LMArena Text: expert | Not measured | 1149 | Only GPT-4 measured |
| LMArena Text: hard prompts in English | Not measured | 1209 | Only GPT-4 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 80.0% | Not measured | Only Gemini 3.1 Flash Lite measured |
| FrontierMath, tiers 1 to 3 | 27.7% | Not measured | Only Gemini 3.1 Flash Lite measured |
| GSM8K | Not measured | 92.0% | Only GPT-4 measured |
| LMArena Text: math | Not measured | 1230 | Only GPT-4 measured |
| LMArena Text: mathematical work | Not measured | 1228 | Only GPT-4 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 56.2% | Only GPT-4 measured |
| MMLU | Not measured | 86.4% | Only GPT-4 measured |
| AI2 Reasoning Challenge | Not measured | 96.3% | Only GPT-4 measured |
| LMArena Text: science | Not measured | 1177 | Only GPT-4 measured |
| LMArena Text: medicine and healthcare | Not measured | 1145 | Only GPT-4 measured |
| LMArena Text: legal and government | Not measured | 1191 | Only GPT-4 measured |
| LMArena Text: business and finance | Not measured | 1145 | Only GPT-4 measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.1 Flash Lite | GPT-4 | Leader |
|---|---|---|---|
| IFBench | 77.2% | 33.2% | Gemini 3.1 Flash Lite |
| Long context reasoning (AA-LCR) | 74.3% | Not measured | Only Gemini 3.1 Flash Lite measured |
| DTBench | 76.8% | 62.7% | Gemini 3.1 Flash Lite |
| LMCA | 35.0% | 17.1% | Gemini 3.1 Flash Lite |
| HellaSwag | Not measured | 95.3% | Only GPT-4 measured |
| WinoGrande | Not measured | 87.5% | Only GPT-4 measured |
| LMArena Text | Not measured | 1206 | Only GPT-4 measured |
| LMArena Text: instruction following | Not measured | 1201 | Only GPT-4 measured |
| LMArena Text: creative writing | Not measured | 1191 | Only GPT-4 measured |
| LMArena Text: English | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: non-English | Not measured | 1194 | Only GPT-4 measured |
| LMArena Text: Chinese | Not measured | 1184 | Only GPT-4 measured |
| LMArena Text: German | Not measured | 1198 | Only GPT-4 measured |
| LMArena Text: Japanese | Not measured | 1137 | Only GPT-4 measured |
| LMArena Text: Korean | Not measured | 1087 | Only GPT-4 measured |
| LMArena Text: French | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: Spanish | Not measured | 1197 | Only GPT-4 measured |
| LMArena Text: Russian | Not measured | 1195 | Only GPT-4 measured |
| LMArena Text: Polish | Not measured | 1265 | Only GPT-4 measured |
| LMArena Text: multi-turn | Not measured | 1206 | Only GPT-4 measured |
| LMArena Text: longer queries | Not measured | 1190 | Only GPT-4 measured |
| LMArena Text: writing and language | Not measured | 1219 | Only GPT-4 measured |
| LMArena Text: entertainment and media | Not measured | 1184 | Only GPT-4 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 | Gemini 3.1 Flash Lite | GPT-4 | 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
Gemini 3.1 Flash Lite costs less
Repository review
50K input + 3K output tokens
Gemini 3.1 Flash Lite costs less
Agent loop
200K input + 10K output tokens
Gemini 3.1 Flash Lite 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 Gemini 3.1 Flash Lite | OpenAI GPT-4 |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | May 7, 2026 | May 28, 2023 |
| Context window | 1.05M | 8K |
| Input $/Mtok | $0.25 | $30.00 |
| Output $/Mtok | $1.50 | $60.00 |
| 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 Gemini 3.1 Flash Lite better than GPT-4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Gemini 3.1 Flash Lite has the edge where output tokens cost 40.0x less, and where the context window is larger, 1.05M against 8K tokens.
Which is cheaper, Gemini 3.1 Flash Lite or GPT-4?
Gemini 3.1 Flash Lite costs $0.25 per million input tokens and $1.50 per million output tokens; GPT-4 costs $30.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemini 3.1 Flash Lite or GPT-4?
Gemini 3.1 Flash Lite, with 1.05M tokens against 8K.
How many benchmarks is the Gemini 3.1 Flash Lite vs GPT-4 comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. Gemini 3.1 Flash Lite's coding index rests on 0 independent benchmarks and GPT-4's on 0. Where only one model has a result, the page says so instead of naming a winner.