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Track Meta model scores across public AI benchmarks including SWE-bench Pro, GPQA Diamond, AIME (OTIS Mock), AAII, and ECI. Each result is shown one benchmark at a time, with source links and evaluation dates — no blended score or composite ranking. 5 models tracked, spanning Llama and Muse.
Models 5
Muse Spark 1.1
Muse · muse-spark-1.1
2026-07-09
6 results
Muse Spark
Muse · muse-spark
2026-04-08
21 results
Llama 4 Maverick
Llama · llama 4 maverick
2025-04-05
28 results
Llama 4 Scout
Llama · llama 4 scout
2025-04-05
18 results
Llama 3.1 405B
Llama · llama 3.1 405b
2024-07-23
16 results
Progress by benchmark
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This view shows SWE-bench Pro (% resolved) only. Other benchmarks use different metrics and are not directly comparable.
Progress matrix
| Model | SWE-bench Verified % resolved | Terminal-Bench 2.1 task success | DeepSWE % resolved | GPQA Diamond accuracy | LiveCodeBench Pro Codeforces Elo | Humanity's Last Exam accuracy | LiveBench score | SWE-bench Pro % resolved | Berkeley Function Calling Leaderboard accuracy | MMMU-Pro accuracy | LMArena source-defined rating | ARC-AGI-1 accuracy | ARC-AGI-2 accuracy | ARC-AGI-3 accuracy | FrontierMath accuracy | AIME (OTIS Mock) accuracy | SimpleQA Verified accuracy | GBA Eval overall score | WeirdML average accuracy | MCP Atlas pass rate | Remote Labor Index automation rate | Artificial Analysis Intelligence Index Index | Epoch Capabilities Index Index | Aider Polyglot % correct | SWE-rebench Resolved rate (pass@1) | MMLU-Pro accuracy | OSWorld task success rate | GAIA: A Benchmark for General AI Assistants accuracy | BrowseComp accuracy | τ²-bench (Telecom) pass^1 | AIME 2026 accuracy | MathVista accuracy | Video-MME accuracy | GDPval Elo | LiveCodeBench Pass@1 | METR Task-Completion Time Horizons 50% time horizon | SciCode accuracy | MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark) accuracy | AA-Omniscience: Knowledge and Hallucination Benchmark AA-Omniscience Index | IFBench accuracy | MultiChallenge accuracy | OpenAI-MRCR v2 (Multi-Round Coreference Resolution) accuracy (mean SequenceMatcher similarity) | LongBench v2 accuracy | Global-MMLU accuracy | Video-MMMU accuracy | WebDev Arena Elo | Search Arena Elo | Arena-Hard-Auto v2.0 % win rate | EQ-Bench Creative Writing v3 Elo | Design Arena Elo | AILuminate AI Safety Benchmark Safety grade | MASK (Model Alignment between Statements and Knowledge) Honesty score | MCP-Universe Overall Success Rate | CharXiv accuracy | OCRBench v2 accuracy | ScreenSpot-Pro accuracy | FACTS Grounding Grounding accuracy | BigCodeBench calibrated Pass@1 | SWE-bench Multilingual % resolved | SWE-bench Multimodal % resolved | SuperGPQA accuracy | EnigmaEval accuracy | ZeroBench accuracy | IMO-Bench accuracy | PutnamBench Problems solved | MathArena HMMT February 2026 accuracy | FrontierMath Tier 4 accuracy | Vectara Hallucination Leaderboard Hallucination Rate | Gray Swan Arena (Agent Red-Teaming / Indirect Prompt Injection) Attack Success Rate (ASR) | PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts Difficulty-Weighted Accuracy (DW-ACC) | Vibe Code Bench Overall accuracy | Online-Mind2Web Task success rate | WebArena Task success rate | GSO: Software Optimization Benchmark for SWE-Agents Opt@1 | MultiNRC accuracy | Terminal-Bench 2.0 task success | SWE-Marathon resolution rate (pass@1) | FrontierCode weighted score (Diamond) | FrontierSWE dominance score | ProgramBench almost-resolved rate | CursorBench score | PostTrainBench weighted average score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Llama 3.1 405B Llama | — | — | — | 50.9% | — | — | — | 11.18% | — | — | — | — | — | — | — | 9.7% | — | — | — | — | — | 17.4 | 129.1 | — | — | 73.2% | — | — | — | — | — | — | — | 255 | 30.5% | — | 29.9% | — | -17 | — | — | — | — | — | — | — | — | — | 953 | — | Good | 61.40 | — | — | — | — | — | 30.4% | — | — | 43.14% | — | — | — | — | — | — | — | 5.89% | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Llama 4 Scout Llama | — | — | — | 51.8% | — | — | — | — | — | — | — | — | — | — | 0.0% | 7.8% | — | — | — | — | — | 13.5 | 130.6 | — | — | 75.2% | — | — | — | — | — | 70.7% | — | 270 | 29.9% | — | 17.0% | 69.4% | — | — | — | — | — | 74.1% | — | — | — | — | 883 | 844 | — | — | — | — | — | — | — | 16.9% | — | — | — | — | 0.0% (pass@1) | — | — | — | — | 7.7% | — | 20.9 | — | — | — | — | — | — | — | — | — | — | — | — |
| Llama 4 Maverick Llama | — | — | — | 67.0% | 528 | — | — | 5.24% | 37.29% | — | — | — | — | — | 0.69% | 20.6% | — | — | 24.5% | — | — | 18.4 | 133.1 | 15.6% | — | 80.9% | — | 28.6% | — | — | — | 73.7% | — | 435 | 39.7% | — | 33.1% | 73.4% | — | — | — | — | — | 82.5% | — | — | — | 17.2% | 927 | 934 | — | 49.73 | — | — | — | — | — | 29.1% | — | — | — | 0.58% | 0.0% (pass@1) | — | — | — | — | 8.2% | — | 26.1 | — | — | — | — | 8.44% | — | — | — | — | — | — | — |
| Muse Spark Muse | — | — | — | 89.8% | — | — | — | 55.00% | — | 80.4% | 1474 | — | — | — | 39.0% | 88.9% | 66.3% | — | — | — | — | 52.1 | 155.1 | — | — | — | — | — | — | 91.5% | — | — | — | 1417 | — | — | — | — | 4 | 75.9% | 75.52% | — | — | — | — | 1508 | — | — | — | 1306 | — | — | — | 86.4% | — | 84.1% | — | — | — | — | — | — | — | — | — | — | 14.6% | — | — | — | 19.67% | — | — | — | 59.05% | — | — | — | — | — | — | — |
| Muse Spark 1.1 Muse | — | 80.0% | — | — | — | 52.2% | — | 61.5% | — | — | — | — | — | — | — | — | — | — | — | 88.1% | — | — | — | — | — | — | 80.8% | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 54.1% | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
Scores are not normalised across benchmarks. Each column uses its own metric. Compare columns independently.