Model comparison
Llama 4 Maverick vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 30.9 on the Noometry Index. Llama 4 Maverick costs 3.7× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. Llama 4 Maverick 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 10.1.
- The biggest single-benchmark swing is ARC-AGI-1: 4.4% for Llama 4 Maverick and 87.5% for Qwen3.8 27B.
- Llama 4 Maverick is cheaper at $0.19 / $0.65 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- Qwen3.8 27B accepts more context: 262K tokens versus 128K.
Side by side
| Llama 4 Maverick | Qwen3.8 27B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.9 | 46.0 |
| Released | 2025-04-05 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 33K |
| Input $ / M tokens | $0.19 | $0.99 |
| Output $ / M tokens | $0.65 | $1.49 |
| Results tracked | 54 | 31 |
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Category by category
Coding Qwen3.8 27B leads
Llama 4 Maverick: 26.6 (#324), Qwen3.8 27B: 50.5 (#44)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| SciCode | 33.1% | 46.6% |
| LMArena Coding | 1302 | 1482 |
| SWE-bench Verified (bash only) | 21% | — |
| Aider Polyglot | 15.6% | — |
| LMArena WebDev | — | 1593 |
| WeirdML | 24.5% | — |
| BigCodeBench Instruct | 49.7% | — |
| BigCodeBench Complete | 61.4% | — |
| ALE-Bench | 172.97 | — |
Agentic & Tool Use Qwen3.8 27B leads
Llama 4 Maverick: 28.2 (#91), Qwen3.8 27B: 32.9 (#57)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
| Berkeley Function Calling Leaderboard | 37.3% | — |
Reasoning Qwen3.8 27B leads
Llama 4 Maverick: 10.1 (#342), Qwen3.8 27B: 41.0 (#54)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 0% | 42.4% |
| NYT Connections (extended) | 8% | 54.5% |
| ARC-AGI-1 | 4.4% | 87.5% |
| CritPt | 0% | 5.4% |
| LMArena Hard Prompts | 1281 | 1460 |
| DTBench | 61.9% | 88% |
| LMCA | 15.9% | 41.4% |
| Epoch Capabilities Index | 132.2 | 149.38 |
| SimpleBench | 27.7% | — |
| Kagi LLM Benchmark | 55.9% | — |
| EnigmaEval | 0.6% | — |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 57.5 | — |
Math Qwen3.8 27B leads
Llama 4 Maverick: 26.0 (#262), Qwen3.8 27B: 37.1 (#161)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1299 | 1456 |
| OTIS Mock AIME 2024-2025 | 20.6% | — |
| ProofBench | — | 16% |
| Omni-MATH | 42.2% | — |
| MATH Level 5 | 73% | — |
| FrontierMath (Feb 2025 set) | 0.7% | — |
Knowledge Qwen3.8 27B leads
Llama 4 Maverick: 33.4 (#204), Qwen3.8 27B: 41.6 (#109)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1259 | 1482 |
| GPQA Diamond | 67% | — |
| Humanity's Last Exam | 5.7% | — |
| MMLU-Pro | 81% | — |
| Confabulations | 22.6% | — |
| Vectara Hallucination Rate | 8.2% | — |
| GPQA (HELM) | 65% | — |
Multimodal Qwen3.8 27B leads
Llama 4 Maverick: 31.6 (#105), Qwen3.8 27B: 41.3 (#37)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1142 | 1271 |
| GeoBench | 52% | — |
| SpatialViz-Bench | 31.8% | — |
Multilingual Qwen3.8 27B leads
Llama 4 Maverick: 42.2 (#195), Qwen3.8 27B: 53.7 (#60)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1269 | 1430 |
| LMArena Chinese | 1277 | 1504 |
| LMArena French | 1259 | 1465 |
| LMArena German | 1291 | 1438 |
| LMArena Japanese | 1207 | 1384 |
| LMArena Korean | 1203 | 1393 |
| LMArena Russian | 1286 | 1415 |
| LMArena Spanish | 1293 | 1448 |
Instruction Following Qwen3.8 27B leads
Llama 4 Maverick: 71.7 (#146), Qwen3.8 27B: 75.8 (#53)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1267 | 1439 |
| IFEval | 90.8% | — |
Long Context Qwen3.8 27B leads
Llama 4 Maverick: 31.4 (#279), Qwen3.8 27B: 44.3 (#70)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1280 | 1450 |
| Fiction.LiveBench | 46.2% | — |
Writing & Preference Qwen3.8 27B leads
Llama 4 Maverick: 38.8 (#252), Qwen3.8 27B: 65.8 (#43)
| Benchmark | Llama 4 Maverick | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1287 | 1441 |
| LMArena Creative Writing | 1267 | 1384 |
| EQ-Bench Creative Writing | 860 | 1671 |
| LMArena Multi-Turn | 1289 | 1441 |
| Short-Story Creative Writing | 62% | — |
| WildBench | 80% | — |
Frequently asked questions
Is Llama 4 Maverick better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 30.9 on the Noometry Index. Llama 4 Maverick costs 3.7× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Maverick or Qwen3.8 27B?
Llama 4 Maverick is cheaper. It lists at $0.19 per million input tokens and $0.65 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is Llama 4 Maverick or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 128K.
How many benchmarks do Llama 4 Maverick and Qwen3.8 27B share?
27 benchmarks have published results for both models. Llama 4 Maverick has 54 scored results on Noometry and Qwen3.8 27B has 31.