Model comparison
Llama 3.1-8B vs Llama 4 Maverick
Llama 4 Maverick is the stronger model overall, scoring 30.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 5.3× less per token, which makes it the better buy when Llama 4 Maverick's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
MetaSummary
- They share 35 benchmarks with published results for both. Llama 3.1-8B scores higher in 2 categories and Llama 4 Maverick in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Maverick leads 33.4 to 8.0.
- The biggest single-benchmark swing is MATH Level 5: 22.9% for Llama 3.1-8B and 73% for Llama 4 Maverick.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.19 / $0.65 for Llama 4 Maverick.
Side by side
| Llama 3.1-8B | Llama 4 Maverick | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 23.0 | 30.9 |
| Released | 2024-07-23 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $0.05 | $0.19 |
| Output $ / M tokens | $0.08 | $0.65 |
| Results tracked | 43 | 54 |
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Category by category
Coding Llama 4 Maverick leads
Llama 3.1-8B: 20.2 (#340), Llama 4 Maverick: 26.6 (#324)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| SciCode | 13.2% | 33.1% |
| WeirdML | 1.7% | 24.5% |
| BigCodeBench Instruct | 32.8% | 49.7% |
| LMArena Coding | 1195 | 1302 |
| BigCodeBench Complete | 40.5% | 61.4% |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| ALE-Bench | — | 172.97 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Llama 4 Maverick leads
Llama 3.1-8B: 22.5 (#131), Llama 4 Maverick: 28.2 (#91)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | 37.3% |
| BALROG | 15.1% | — |
Reasoning Llama 3.1-8B leads
Llama 3.1-8B: 14.9 (#321), Llama 4 Maverick: 10.1 (#342)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1175 | 1281 |
| DTBench | 50.9% | 61.9% |
| LMCA | 5.4% | 15.9% |
| Epoch Capabilities Index | 116.57 | 132.2 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| Kagi LLM Benchmark | — | 55.9% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| Chess Puzzles | 0% | — |
| EnigmaEval | — | 0.6% |
| ForecastBench | — | 57.5 |
| PIQA | 81.2% | — |
Math Llama 4 Maverick leads
Llama 3.1-8B: 10.2 (#317), Llama 4 Maverick: 26.0 (#262)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 20.6% |
| Omni-MATH | 13.7% | 42.2% |
| LMArena Math | 1179 | 1299 |
| MATH Level 5 | 22.9% | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
| GSM8K | 82.4% | — |
Knowledge Llama 4 Maverick leads
Llama 3.1-8B: 8.0 (#307), Llama 4 Maverick: 33.4 (#204)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| GPQA Diamond | 27% | 67% |
| MMLU-Pro | 40.6% | 81% |
| GPQA (HELM) | 24.7% | 65% |
| LMArena Expert | 1144 | 1259 |
| Humanity's Last Exam | — | 5.7% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multimodal Not comparable
Llama 3.1-8B: —, Llama 4 Maverick: 31.6 (#105)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | — | 1142 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |
Multilingual Llama 4 Maverick leads
Llama 3.1-8B: 34.0 (#249), Llama 4 Maverick: 42.2 (#195)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1148 | 1269 |
| LMArena Chinese | 1151 | 1277 |
| LMArena French | 1177 | 1259 |
| LMArena German | 1144 | 1291 |
| LMArena Japanese | 1061 | 1207 |
| LMArena Korean | 1053 | 1203 |
| LMArena Russian | 1158 | 1286 |
| LMArena Spanish | 1169 | 1293 |
Instruction Following Llama 4 Maverick leads
Llama 3.1-8B: 58.9 (#258), Llama 4 Maverick: 71.7 (#146)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| IFEval | 74.3% | 90.8% |
| LMArena Instruction Following | 1159 | 1267 |
Long Context Llama 3.1-8B leads
Llama 3.1-8B: 35.8 (#238), Llama 4 Maverick: 31.4 (#279)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1182 | 1280 |
| Fiction.LiveBench | — | 46.2% |
Writing & Preference Llama 4 Maverick leads
Llama 3.1-8B: 29.7 (#290), Llama 4 Maverick: 38.8 (#252)
| Benchmark | Llama 3.1-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1187 | 1287 |
| LMArena Creative Writing | 1154 | 1267 |
| EQ-Bench Creative Writing | 713 | 860 |
| WildBench | 68.7% | 80% |
| LMArena Multi-Turn | 1172 | 1289 |
| Short-Story Creative Writing | — | 62% |
Frequently asked questions
Is Llama 3.1-8B better than Llama 4 Maverick?
Llama 4 Maverick is the stronger model overall, scoring 30.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 5.3× less per token, which makes it the better buy when Llama 4 Maverick's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or Llama 4 Maverick?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Llama 4 Maverick lists at $0.19 and $0.65.
Is Llama 3.1-8B or Llama 4 Maverick better for coding?
Llama 4 Maverick scores higher on coding benchmarks: 26.6 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Llama 3.1-8B and Llama 4 Maverick share?
35 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Llama 4 Maverick has 54.