HLE
21.4 score
LLM Leaderboard · Open Weight
Qwen3.6-35B-A3B benchmarks, pricing, and LLM comparison.
Compare Qwen3.6-35B-A3B vs GPT, Claude, Gemini, DeepSeek, open-weight, and frontier AI models using public benchmark scores, token pricing, context window, and access details.
Rank #29AskClash overall score: 46.9
$0 / $0Input and output token price, when published. Context: 262K.
APIBilling and access path cached for this model row.
Qwen3.6-35B-A3B benchmark snapshot
AskClash combines public LLM benchmark cells into a weighted percentile score and penalizes missing coverage so narrow rows do not dominate better-measured models.
Overall46.9
Benchmark cells10
Context262K
CreatorAlibaba
Qwen3.6-35B-A3B public benchmark scores
Cached benchmark values can include HLE, GPQA, SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, MCP Atlas, MMMU-Pro, ARC-AGI-2, Tau2, and model-specific coding or agent scores.
GPQA
86.0 score
IFEval
64.4 score
SWE-bench
73.4 score
Terminal-Bench
51.5 score
LiveCodeBench
80.4 score
MCP Atlas
62.8 score
CharXiv
78.0 score
MMMU-Pro
75.3 score
Tau2
95.3 score
Qwen3.6-35B-A3B vs other AI models
Use these comparison links to evaluate Qwen3.6-35B-A3B against nearby LLMs by benchmark score, price, context window, and provider.
Related AI and tech coverage
Cached AskClash article matches that can provide release, provider, benchmark, pricing, or market context around this model.
Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7
Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7 Simon Willison’s Weblog Subscribe Sponsored by: Teleport — Connect agents to your infra in seconds with Teleport Beams. Built-in identity. Zero secrets. Get early access Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7 16th April 2026 For anyone who has been (inadvisably) taking my pelican riding a bicycle benchmark seriously as a robust way to test models, here are pelicans from this morning’s t
HuggingFace TRL / RLHF v1.3.0 Release Notes
TRL v1.3 ships training support for the new **Qwen 3.6** family (`Qwen/Qwen3.6-27B`, `Qwen/Qwen3.6-35B-A3B`). Qwen 3.6 reuses the `Qwen3_5Moe*` architecture but ships a slightly different chat template (adds a `preserve_thinking` flag, tweaks tool-arg stringification), so exact-string template matching needed updates across the stack. A new experimental `TPOTrainer` implements [Triple Preference Optimization](https://huggingface.co/papers/2405.16681), which augments DPO with a `reference` (gold)
Alibaba Unveils New AI Chip, Upgrades AI Model
The company expects AI-related product revenue to count for 50% of cloud unit's external revenue in about a year, and become the primary driver of revenue growth for that unit, chief executive Eddie Wu said earlier this month. Alibaba's management said in a recent earnings call that scaling up the deployment of its in-house chips represent "the highest value for money compute power," which will improve Alibaba Cloud's margins.
Embedding Tradeoffs, Quantified
Embedding Tradeoffs, Quantified | Vespa Blog { } Blog Vespa.ai Docs Subscribe Vespa Blog We Make AI Work Share Thomas H. Thoresen Senior Software Engineer 14 Jan 2026 Embedding Tradeoffs, Quantified Created by NanoBanana Most Vespa users run hybrid search - combining BM25 (and/or other lexical features) with semantic vectors. But which embedding model should you use? And how do you balance cost, quality, and latency as you scale? The typical approach: open the MTEB leaderboard , find the “Retrie
Last cached leaderboard date: May 25, 2026. This model page is generated from the AskClash LLM Leaderboard cache and linked from the live leaderboard.