AI Agent Benchmarks
No single winner: Opus, GPT-5.3 Codex, and Qwen3 each dominate different tasks
No single model wins every benchmark in February 2026. Claude Opus 4.6 leads SWE-bench Verified (80.8%) and ARC-AGI-2 (68.8%). GPT-5.3 Codex dominates Terminal-Bench 2.0 (77.3%) and SWE-Bench Pro. Qwen3-Coder-Next achieves 70.6% SWE-bench with only 3B active parameters under Apache 2.0 — a remarkable efficiency breakthrough. Gemini 3 Flash scores 76.2% SWE-bench at 33x less cost than Opus. The key insight: different tasks need different models.
80.8%
Opus 4.6 SWE-bench Verified
SWE-bench
77.3%
GPT-5.3 Codex Terminal-Bench
Terminal-Bench
68.8%
Opus 4.6 ARC-AGI-2 (#1)
ARC Prize
70.6%
Qwen3-Coder (3B params!)
SWE-bench
Benchmark Leaderboard (February 2026)
The benchmark landscape reveals specialization over generalization. No single model dominates every category — the winners depend on the task type.
SWE-bench Verified
#1 OpusOpus 4.6: 80.8% | Gemini 3 Flash: 76.2% | Qwen3-Coder: 70.6% (3B). Real GitHub bug fixes.
Terminal-Bench 2.0
#1 GPT-5.3GPT-5.3 Codex: 77.3% | Opus 4.6: 65.4% | Opus 4.5: 59.3%. Multi-step agentic coding.
ARC-AGI-2
#1 OpusOpus 4.6: 68.8% | GPT-5.2: 54.2% | Gemini 3: 45.1%. Abstract reasoning (humans avg 60%).
OSWorld
#1 OpusOpus 4.6: 72.7% | Opus 4.5: 66.3%. Computer use tasks across real operating systems.
MMMU-Pro
#1 GeminiGemini 3 Pro: 81.0%. Multimodal understanding across images, charts, documents.
SWE-Bench Pro
#1 GPT-5.3GPT-5.3 Codex: best score | Qwen3-Coder: 44.3%. Complex multi-file engineering.
The Efficiency Revolution: Qwen3-Coder-Next
The most significant benchmark story of early 2026 is not a frontier model — it is Qwen3-Coder-Next achieving 70.6% on SWE-bench Verified with only 3B active parameters under Apache 2.0. This demonstrates that coding capability is increasingly achievable at dramatically smaller scale, with profound implications for edge deployment, cost optimization, and open-source accessibility.
70.6% SWE-bench
3B ParamsMatches models 10-100x larger on real-world coding tasks. Apache 2.0 license.
44.3% SWE-Bench Pro
EfficientCompetitive on complex multi-file tasks — remarkable for a 3B parameter model.
Gemini 3 Flash
33x Cheaper76.2% SWE-bench at 33x less cost than Opus. Cost-efficiency is the emerging battleground.
The Benchmark-Production Gap
High benchmark scores do not guarantee production success. Key reasons: benchmarks test isolated tasks (production requires coordination), benchmarks have clean inputs (production has messy data), benchmarks measure accuracy (production also needs latency, cost, reliability). Custom evaluation frameworks (RAGAS, DeepEval) that mirror production conditions are essential. Tool-use reliability (tau-bench) is a better predictor of production success than general reasoning.
Isolated vs Coordinated
Gap 1Benchmarks test single tasks. Production requires multi-step coordination across tools and services.
Clean vs Messy
Gap 2Benchmark inputs are well-formatted. Production data is noisy, incomplete, and adversarial.
Accuracy vs Everything
Gap 3Production needs latency, cost, reliability, and graceful degradation — not just correctness.
Key Findings
No single model wins every benchmark — Opus leads reasoning/SE, GPT-5.3 leads agentic coding, Gemini leads multimodal
GPT-5.3 Codex dominates Terminal-Bench 2.0 at 77.3%, surpassing Opus 4.6 (65.4%)
Claude Opus 4.6 leads SWE-bench Verified at 80.8% and ARC-AGI-2 at 68.8%
Qwen3-Coder-Next achieves 70.6% SWE-bench with only 3B parameters (Apache 2.0) — efficiency breakthrough
Gemini 3 Flash scores 76.2% SWE-bench at 33x less cost than Opus — cost-efficiency is the new frontier
ARC-AGI-2: humans average 60%, Opus 4.6 at 68.8% exceeds average human performance
Tool-use reliability (tau-bench) is a better predictor of production success than general reasoning benchmarks
Custom evaluation frameworks (RAGAS, DeepEval) outperform standard benchmarks for production readiness
Frequently Asked Questions
Claude Opus 4.6 leads ARC-AGI-2 at 68.8%, an 83% relative improvement over Opus 4.5's 37.6%.
Sources & References
6 validated sources · Last updated 2026-02-06