Model comparison
Llama 3.1-8B vs Qwen3-Coder 480B-A35B Instruct
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 37.7 to 22.8 on the Noometry Index. Llama 3.1-8B costs 52× 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 3.1-8B scores higher in 0 categories and Qwen3-Coder 480B-A35B Instruct in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3-Coder 480B-A35B Instruct leads 36.4 to 8.0.
- The biggest single-benchmark swing is WeirdML: 1.7% for Llama 3.1-8B and 41.2% for Qwen3-Coder 480B-A35B Instruct.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 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 3.1-8B | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 22.8 | 37.7 |
| Released | 2024-07-23 | 2025-04 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.05 | $1.50 |
| Output $ / M tokens | $0.08 | $7.50 |
| Results tracked | 43 | 25 |
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Category by category
Coding Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 19.9 (#335), Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| WeirdML | 1.7% | 41.2% |
| LMArena Coding | 1195 | 1413 |
| SWE-bench Verified (bash only) | — | 55.4% |
| LMArena WebDev | — | 1275 |
| SciCode | 13.2% | — |
| GSO | — | 4.9% |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 22.3 (#123), Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 14.5 (#316), Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.48 | — |
| PIQA | 81.2% | — |
Math Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 9.5 (#311), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1179 | 1364 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 8.0 (#302), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1144 | 1337 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 34.2 (#244), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1148 | 1345 |
| LMArena Chinese | 1151 | 1356 |
| LMArena French | 1176 | 1394 |
| LMArena German | 1144 | 1324 |
| LMArena Japanese | 1061 | 1309 |
| LMArena Korean | 1053 | 1307 |
| LMArena Russian | 1158 | 1367 |
| LMArena Spanish | 1169 | 1360 |
Instruction Following Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 59.1 (#253), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1159 | 1356 |
| IFEval | 74.3% | — |
Long Context Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 35.8 (#233), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1182 | 1379 |
Writing & Preference Qwen3-Coder 480B-A35B Instruct leads
Llama 3.1-8B: 29.9 (#285), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1187 | 1356 |
| LMArena Creative Writing | 1154 | 1333 |
| LMArena Multi-Turn | 1172 | 1365 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 37.7 to 22.8 on the Noometry Index. Llama 3.1-8B costs 52× 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 3.1-8B or Qwen3-Coder 480B-A35B Instruct?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is Llama 3.1-8B or Qwen3-Coder 480B-A35B Instruct better for coding?
Qwen3-Coder 480B-A35B Instruct scores higher on coding benchmarks: 36.2 versus 19.9 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 3.1-8B and Qwen3-Coder 480B-A35B Instruct share?
18 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.