# Gemini 3.8 Flash vs GPT-5.5

> GPT-5.5 is the stronger model overall, scoring 63.4 to 59.5 on the Noometry Index. Gemini 3.8 Flash costs 7.5× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-5
- Last updated: 2026-10-10
- Shared benchmarks: 43

## Summary

- They share 43 benchmarks with published results for both. Gemini 3.8 Flash scores higher in 3 categories and GPT-5.5 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.5 leads 81.5 to 65.2.
- The biggest single-benchmark swing is FrontierMath Tier 4: 22% for Gemini 3.8 Flash and 72.5% for GPT-5.5.
- Gemini 3.8 Flash is cheaper at $0.75 / $3.75 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 1.05M.

## Snapshot

| | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 59.5 | 63.4 |
| Rank | 13 | 8 |
| Context | 1.05M | 1.05M |
| Input $/M | $0.75 | $5 |
| Output $/M | $3.75 | $30 |
| Weights | Proprietary | Proprietary |

## Coding

- Gemini 3.8 Flash: 56.4 (#17)
- GPT-5.5: 58.3 (#13)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| DeepSWE | 73.8% | 67% |
| FrontierCode | 41.2% | 43% |
| LMArena WebDev | 1581 | 1512 |
| SciCode | 56.6% | 56.1% |
| LMArena Coding | 1512 | 1494 |
| ALE-Bench | 1,270 | 1,943 |
| SWE-bench Verified | — | 80.6% |
| CursorBench | 39.6% | — |
| FrontierSWE | 19.6% | — |
| GSO | — | 40.2% |
| WeirdML | — | 84.9% |
| MirrorCode | — | 10% |

## Agentic & Tool Use

- Gemini 3.8 Flash: 40.3 (#22)
- GPT-5.5: 50.7 (#6)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| APEX-Agents | 64.3% | 55.1% |
| GDP.pdf | 23.4% | 26% |
| Vending-Bench 2 | 5,094 | 7,524 |
| Terminal-Bench | — | 84.7% |
| OSWorld 2.0 | — | 13% |
| Remote Labor Index | — | 6.3% |
| τ²-bench Banking | — | 44.6% |
| DeepResearch Bench | — | 54% |
| PostTrainBench | — | 27.2% |
| ExploitBench | — | 47.4% |
| GBAEval | — | 53.2% |
| LMArena Search | — | 1242 |

## Reasoning

- Gemini 3.8 Flash: 64.7 (#13)
- GPT-5.5: 72.7 (#8)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| NYT Connections (extended) | 97.4% | 96.2% |
| CritPt | 18.3% | 27.1% |
| Chess Puzzles | 61% | 54% |
| LMArena Hard Prompts | 1506 | 1489 |
| Mystery Game Puzzles | 47% | 56% |
| DTBench | 95.7% | 96% |
| LMCA | 52.9% | 54.3% |
| Surface Evolver Bench | 76.9% | 88.1% |
| Epoch Capabilities Index | 157.13 | 159.25 |
| ARC-AGI-2 | — | 85% |
| SimpleBench | — | 69% |
| Kagi LLM Benchmark | — | 88.8% |
| ARC-AGI-1 | — | 95% |
| EBR-Bench | — | 34.3% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | — | 60.6 |

## Math

- Gemini 3.8 Flash: 65.2 (#22)
- GPT-5.5: 81.5 (#9)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.4% | 85.3% |
| FrontierMath Tier 4 | 22% | 72.5% |
| OTIS Mock AIME 2024-2025 | 98.9% | 100% |
| ProofBench | 48% | 50% |
| LMArena Math | 1528 | 1486 |
| MathArena Final-Answer Competitions | — | 94.3% |
| FrontierMath (Feb 2025 set) | — | 51.7% |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | — | 35.4% |

## Knowledge

- Gemini 3.8 Flash: 74.9 (#2)
- GPT-5.5: 64.4 (#14)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| GPQA Diamond | 95.4% | 94% |
| SimpleQA Verified | 69.7% | 63% |
| LMArena Expert | 1529 | 1508 |
| Humanity's Last Exam | 44.5% | — |
| Vectara Hallucination Rate | — | 9.3% |

## Multimodal

- Gemini 3.8 Flash: 40.9 (#33)
- GPT-5.5: 47.1 (#11)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| LMArena Vision | 1312 | 1297 |
| Blueprint-Bench 2 | 38.6% | 36.2% |
| Furniture Assembly | 31.7% | 44.2% |
| LMArena Document | — | 1486 |

## Multilingual

- Gemini 3.8 Flash: 57.9 (#5)
- GPT-5.5: 56.5 (#19)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 1486 | 1467 |
| LMArena Chinese | 1547 | 1535 |
| LMArena French | 1502 | 1486 |
| LMArena German | 1470 | 1478 |
| LMArena Japanese | 1505 | 1498 |
| LMArena Korean | 1479 | 1461 |
| LMArena Russian | 1507 | 1473 |
| LMArena Spanish | 1492 | 1469 |

## Instruction Following

- Gemini 3.8 Flash: 78.2 (#11)
- GPT-5.5: 77.7 (#18)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 1489 | 1479 |

## Long Context

- Gemini 3.8 Flash: 46.2 (#23)
- GPT-5.5: 48.3 (#12)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| LMArena Longer Query | 1506 | 1484 |
| CL-bench Life | — | 22.2% |

## Writing & Preference

- Gemini 3.8 Flash: 72.1 (#16)
- GPT-5.5: 72.8 (#13)

| Benchmark | Gemini 3.8 Flash | GPT-5.5 |
|---|---|---|
| LMArena Text | 1494 | 1471 |
| LMArena Creative Writing | 1485 | 1455 |
| EQ-Bench Creative Writing | 1748 | 1844 |
| LMArena Multi-Turn | 1503 | 1477 |
| EQ-Bench 4 | — | 1315 |

## FAQ

### Is Gemini 3.8 Flash better than GPT-5.5?

GPT-5.5 is the stronger model overall, scoring 63.4 to 59.5 on the Noometry Index. Gemini 3.8 Flash costs 7.5× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

### Which is cheaper, Gemini 3.8 Flash or GPT-5.5?

Gemini 3.8 Flash is cheaper. It lists at $0.75 per million input tokens and $3.75 per million output tokens; GPT-5.5 lists at $5 and $30.

### Is Gemini 3.8 Flash or GPT-5.5 better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.3 versus 56.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 1.05M.

### How many benchmarks do Gemini 3.8 Flash and GPT-5.5 share?

43 benchmarks have published results for both models. Gemini 3.8 Flash has 46 scored results on Noometry and GPT-5.5 has 71.