Latest
Qwen3.8-Flash-Next releasedAlibaba · Aug 26, 2026
Source confirmed
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start the free Radar BriefLLM leaderboard, August 2026
Compare frontier AI models by quality, cost, and context. 105 Supported and 121 Estimated models among 403 tracked LLMs — 406 benchmarks, real pricing, and runtime data in one place.
Search models and benchmarksFree Radar Brief ranks up to five confirmed market changes and keeps the original source attached. No card required.
Latest
Qwen3.8-Flash-Next releasedAlibaba · Aug 26, 2026
Source confirmed
Z.AI · Aug 26, 2026
Apodex · Aug 24, 2026
Current models that clear the ranking, freshness, and evidence thresholds for each decision.
Benchmarks, pricing, runtime signals, and context window in one table. Filter state syncs to the URL so every view is shareable. Supported and Estimated labels show how much independent evidence backs each position.
Reading the table
The BenchLM LLM leaderboard 2026 ranks 226+ models and tracks 403+ large language models side by side across 406 benchmarks — from SWE-bench and LiveCodeBench for coding to GPQA Diamond and MMLU-Pro for knowledge and reasoning. Whether you need the best AI models 2026 has to offer for agentic workflows, math, multilingual tasks, or instruction following, our AI benchmark comparison tables make it easy to see how GPT-5, Claude, Gemini, DeepSeek, Llama, and dozens of other frontier and open-source models stack up on both benchmarks and operator tradeoffs like price and context. Supported and Estimated labels separate evidence strength from score, so incomplete reporting does not automatically remove a model from the comparison.
Embed the live leaderboardDataset journal
Choose any two ranked models. The comparison opens with the decision, exact units, and evidence coverage.
Six-month release record
Track which release led its month, how provider depth changed, and whether the crown moved.
Best ranked model released in Aug 2026
Qwen3.8 MaxAlibabaThe evidence map
Move from the overall ranking into the capability or workload that actually decides your model choice.
Each model's overall score is a normalized weighted average of category averages. Within each category, benchmark results are normalized to a common scale and combined using weights that favor harder, less-saturated evaluations.
Confidence remains separate from score. It shows how much public benchmark evidence supports a result, while display-only benchmarks stay visible for context without receiving ranking weight.
Data comes from OpenBench, official model papers, and public leaderboards. External consensus signals are bounded calibration inputs and are not exposed in exported data.
Read the complete methodologyTerminal-Bench 2.0 · BrowseComp · OSWorld-Verified
3 weighted · 81 context-only
SWE-bench Verified · SWE-Rebench · LiveCodeBench · SWE-bench Pro · SciCode
5 weighted · 52 context-only
LongBench v2 · MRCRv2 · ARC-AGI-2
3 weighted · 24 context-only
MMMU-Pro · OfficeQA Pro · CharXiv
3 weighted · 62 context-only
GPQA · SuperGPQA · MMLU-Pro · HLE · SimpleQA
5 weighted · 41 context-only
MMLU-ProX
1 weighted · 12 context-only
IFEval · IFBench
2 weighted · 2 context-only
AIME26 · HMMT Feb 2026 · FrontierMath v2 (Tiers 1-3) · FrontierMath v2 (Tier 4) · USAMO 2026
5 weighted · 27 context-only