Model comparison
Llama 3.1-70B vs Qwen3-Coder 480B-A35B Instruct
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 29.6 on the Noometry Index. Llama 3.1-70B costs 7.5× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. Llama 3.1-70B scores higher in 1 category and Qwen3-Coder 480B-A35B Instruct in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-Coder 480B-A35B Instruct leads 37.6 to 13.5.
- The biggest single-benchmark swing is WeirdML: 9% for Llama 3.1-70B and 41.2% for Qwen3-Coder 480B-A35B Instruct.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct accepts more context: 262K tokens versus 128K.
Side by side
| Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 29.6 | 38.1 |
| Released | 2024-07-23 | 2025-04 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.40 | $1.50 |
| Output $ / M tokens | $0.40 | $7.50 |
| Results tracked | 35 | 25 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 30.3 (#296), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| WeirdML | 9% | 41.2% |
| LMArena Coding | 1260 | 1412 |
| SWE-bench Verified (bash only) | — | 55.4% |
| LMArena WebDev | — | 1275 |
| GSO | — | 4.9% |
| BigCodeBench Instruct | 46.1% | — |
| BigCodeBench Complete | 54.8% | — |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
Agentic & Tool Use Llama 3.1-70B leads
Llama 3.1-70B: 25.1 (#112), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 21.6 (#220), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| DTBench | 60% | — |
| LMCA | 14.8% | — |
| Epoch Capabilities Index | 125.92 | — |
Math Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 13.5 (#304), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1252 | 1365 |
| OTIS Mock AIME 2024-2025 | 3.6% | — |
| Omni-MATH | 21% | — |
| MATH Level 5 | 36.7% | — |
Knowledge Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 24.2 (#269), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1209 | 1338 |
| GPQA Diamond | 44.2% | — |
| MMLU-Pro | 65.3% | — |
| GPQA (HELM) | 42.6% | — |
| MMLU | 80.1% | — |
Multilingual Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 38.8 (#225), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1219 | 1346 |
| LMArena Chinese | 1215 | 1357 |
| LMArena French | 1261 | 1398 |
| LMArena German | 1222 | 1325 |
| LMArena Japanese | 1132 | 1310 |
| LMArena Korean | 1140 | 1305 |
| LMArena Russian | 1234 | 1366 |
| LMArena Spanish | 1253 | 1360 |
Instruction Following Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 65.3 (#223), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1231 | 1355 |
| IFEval | 82.1% | — |
Long Context Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 37.6 (#214), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1241 | 1378 |
Writing & Preference Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-70B: 35.4 (#267), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | Llama 3.1-70B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1261 | 1357 |
| LMArena Creative Writing | 1232 | 1333 |
| LMArena Multi-Turn | 1256 | 1365 |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
Frequently asked questions
Is Llama 3.1-70B better than Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 29.6 on the Noometry Index. Llama 3.1-70B costs 7.5× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-70B or Qwen3-Coder 480B-A35B Instruct?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is Llama 3.1-70B or Qwen3-Coder 480B-A35B Instruct better for coding?
Qwen3-Coder 480B-A35B Instruct scores higher on coding benchmarks: 35.5 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3-Coder 480B-A35B Instruct does, with 262K tokens against 128K.
How many benchmarks do Llama 3.1-70B and Qwen3-Coder 480B-A35B Instruct share?
18 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.