# GPT-4.1 vs Qwen3-Coder 480B-A35B Instruct

> Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 37.7 to 35.7 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-4-1-vs-qwen3-coder-480b-a35b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GPT-4.1 scores higher in 4 categories and Qwen3-Coder 480B-A35B Instruct in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-Coder 480B-A35B Instruct leads 34.9 to 21.4.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 39.6% for GPT-4.1 and 55.4% for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 / $7.50 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 35.7 | 37.7 |
| Rank | 215 | 186 |
| Context | 1.05M | 262K |
| Input $/M | $2 | $1.50 |
| Output $/M | $8 | $7.50 |
| Weights | Proprietary | Open |

## Coding

- GPT-4.1: 34.2 (#228)
- Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 39.6% | 55.4% |
| WeirdML | 39% | 41.2% |
| LMArena Coding | 1391 | 1413 |
| ALE-Bench | 558.1 | 461.45 |
| SWE-bench Verified | 48.5% | — |
| Aider Polyglot | 52.4% | — |
| LMArena WebDev | — | 1275 |
| GSO | — | 4.9% |
| CadEval | 42% | — |
| AlgoTune | — | 1.44 |

## Agentic & Tool Use

- GPT-4.1: 34.4 (#40)
- Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| Berkeley Function Calling Leaderboard | 54% | — |

## Reasoning

- GPT-4.1: 11.7 (#334)
- Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 49.5% |
| LMArena Hard Prompts | 1384 | 1372 |
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| ARC-AGI-1 | 5.5% | — |
| Chess Puzzles | 6% | — |
| EnigmaEval | 2.2% | — |
| DTBench | 68.3% | — |
| LMCA | 25.6% | — |
| Epoch Capabilities Index | 136.79 | — |
| ForecastBench | 61.5 | — |

## Math

- GPT-4.1: 21.4 (#269)
- Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1369 | 1364 |
| FrontierMath (Tiers 1-3) | 6% | — |
| OTIS Mock AIME 2024-2025 | 38.3% | — |
| Omni-MATH | 47.1% | — |
| MATH Level 5 | 83% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GPT-4.1: 37.0 (#152)
- Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1364 | 1337 |
| GPQA Diamond | 66.9% | — |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| MMLU-Pro | 81.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| GPQA (HELM) | 65.9% | — |

## Multimodal

- GPT-4.1: 37.5 (#66)
- Qwen3-Coder 480B-A35B Instruct: —

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Vision | 1210 | — |
| GeoBench | 72% | — |

## Multilingual

- GPT-4.1: 49.6 (#128)
- Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1370 | 1345 |
| LMArena Chinese | 1382 | 1356 |
| LMArena French | 1383 | 1394 |
| LMArena German | 1380 | 1324 |
| LMArena Japanese | 1320 | 1309 |
| LMArena Korean | 1339 | 1307 |
| LMArena Russian | 1377 | 1367 |
| LMArena Spanish | 1375 | 1360 |

## Instruction Following

- GPT-4.1: 71.5 (#148)
- Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1367 | 1356 |
| IFEval | 83.8% | — |

## Long Context

- GPT-4.1: 40.0 (#158)
- Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1385 | 1379 |
| Fiction.LiveBench | 63.9% | — |

## Writing & Preference

- GPT-4.1: 57.8 (#120)
- Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)

| Benchmark | GPT-4.1 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1383 | 1356 |
| LMArena Creative Writing | 1363 | 1333 |
| LMArena Multi-Turn | 1398 | 1365 |
| EQ-Bench Creative Writing | 1420 | — |
| WildBench | 85.4% | — |

## FAQ

### Is GPT-4.1 better than Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 37.7 to 35.7 on the Noometry Index.

### Which is cheaper, GPT-4.1 or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; GPT-4.1 lists at $2 and $8.

### Is GPT-4.1 or Qwen3-Coder 480B-A35B Instruct better for coding?

Qwen3-Coder 480B-A35B Instruct scores higher on coding benchmarks: 36.2 versus 34.2 in the Noometry coding category.

### Which has the bigger context window?

GPT-4.1 does, with 1.05M tokens against 262K.

### How many benchmarks do GPT-4.1 and Qwen3-Coder 480B-A35B Instruct share?

21 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.