# GPT-4o vs Qwen3.8 27B

> Qwen3.8 27B is the stronger model overall, scoring 46.0 to 28.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-4o-vs-qwen3-8-27b
- Last updated: 2026-10-12
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GPT-4o scores higher in 0 categories and Qwen3.8 27B in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 9.4.
- The biggest single-benchmark swing is ARC-AGI-1: 4.5% for GPT-4o and 87.5% for Qwen3.8 27B.
- Qwen3.8 27B is cheaper at $0.04 / $2.30 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Qwen3.8 27B accepts more context: 262K tokens versus 128K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4o | Qwen3.8 27B |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 28.6 | 46.0 |
| Rank | 324 | 68 |
| Context | 128K | 262K |
| Input $/M | $2.50 | $0.04 |
| Output $/M | $10 | $2.30 |
| Weights | Proprietary | Open |

## Coding

- GPT-4o: 24.8 (#328)
- Qwen3.8 27B: 50.5 (#44)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Coding | 1297 | 1482 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| LMArena WebDev | — | 1593 |
| SciCode | — | 46.6% |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |

## Agentic & Tool Use

- GPT-4o: 21.0 (#141)
- Qwen3.8 27B: 32.9 (#57)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |

## Reasoning

- GPT-4o: 9.4 (#343)
- Qwen3.8 27B: 41.0 (#54)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 0% | 42.4% |
| ARC-AGI-1 | 4.5% | 87.5% |
| CritPt | 0% | 5.4% |
| LMArena Hard Prompts | 1281 | 1460 |
| DTBench | 64.5% | 88% |
| LMCA | 16.6% | 41.4% |
| Epoch Capabilities Index | 128.97 | 149.38 |
| SimpleBench | 17.8% | — |
| NYT Connections (extended) | — | 54.5% |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |

## Math

- GPT-4o: 10.6 (#312)
- Qwen3.8 27B: 37.1 (#161)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1285 | 1456 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| ProofBench | — | 16% |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |

## Knowledge

- GPT-4o: 28.8 (#242)
- Qwen3.8 27B: 41.6 (#109)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1250 | 1482 |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |

## Multimodal

- GPT-4o: 34.5 (#91)
- Qwen3.8 27B: 41.3 (#37)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1137 | 1271 |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |

## Multilingual

- GPT-4o: 43.2 (#186)
- Qwen3.8 27B: 53.7 (#60)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1283 | 1430 |
| LMArena Chinese | 1277 | 1504 |
| LMArena French | 1304 | 1465 |
| LMArena German | 1282 | 1438 |
| LMArena Japanese | 1257 | 1384 |
| LMArena Korean | 1234 | 1393 |
| LMArena Russian | 1286 | 1415 |
| LMArena Spanish | 1292 | 1448 |

## Instruction Following

- GPT-4o: 66.6 (#207)
- Qwen3.8 27B: 75.8 (#53)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1439 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |

## Long Context

- GPT-4o: 39.4 (#179)
- Qwen3.8 27B: 44.3 (#70)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1289 | 1450 |
| Fiction.LiveBench | 66.7% | — |

## Writing & Preference

- GPT-4o: 52.6 (#166)
- Qwen3.8 27B: 65.8 (#43)

| Benchmark | GPT-4o | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1300 | 1441 |
| LMArena Creative Writing | 1292 | 1384 |
| LMArena Multi-Turn | 1302 | 1441 |
| Short-Story Creative Writing | 81.8% | — |
| EQ-Bench Creative Writing | — | 1671 |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |

## FAQ

### Is GPT-4o better than Qwen3.8 27B?

Qwen3.8 27B is the stronger model overall, scoring 46.0 to 28.6 on the Noometry Index.

### Which is cheaper, GPT-4o or Qwen3.8 27B?

Qwen3.8 27B is cheaper. It lists at $0.04 per million input tokens and $2.30 per million output tokens; GPT-4o lists at $2.50 and $10.

### Is GPT-4o or Qwen3.8 27B better for coding?

Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 24.8 in the Noometry coding category.

### Which has the bigger context window?

Qwen3.8 27B does, with 262K tokens against 128K.

### How many benchmarks do GPT-4o and Qwen3.8 27B share?

24 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Qwen3.8 27B has 31.