HLE
24.3 score
LLM Leaderboard · Open Weight
Qwen3.5-27B benchmarks, pricing, and LLM comparison.
Compare Qwen3.5-27B vs GPT, Claude, Gemini, DeepSeek, open-weight, and frontier AI models using public benchmark scores, token pricing, context window, and access details.
Rank #30AskClash overall score: 46.9
$0 / $0Input and output token price, when published. Context: 262K.
APIBilling and access path cached for this model row.
Qwen3.5-27B 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
Qwen3.5-27B 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
85.5 score
IFEval
95.0 score
SWE-bench
72.4 score
Terminal-Bench
41.6 score
LiveCodeBench
80.7 score
OSWorld
56.2 score
CharXiv
79.5 score
MMMU-Pro
75.0 score
Tau2
93.9 score
Qwen3.5-27B vs other AI models
Use these comparison links to evaluate Qwen3.5-27B 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.
Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model Simon Willison’s Weblog Subscribe Sponsored by: Honeycomb — AI agents behave unpredictably. Get the context you need to debug what actually happened. Read the blog 22nd April 2026 - Link Blog Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model ( via ) Big claims from Qwen about their latest open weight model: Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-3
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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
Anthropic Microsoft deal 🤝, Cursor $3B ARR 📈, cloud agent lessons 🤖
Anthropic Microsoft deal 🤝, Cursor $3B ARR 📈, cloud agent lessons 🤖 TLDR Newsletters Advertise Blog TLDR TLDR AI 2026-05-22 Anthropic Microsoft deal 🤝, Cursor $3B ARR 📈, cloud agent lessons 🤖 Defending Against the Next Generation of Agentic AI Attacks. (Sponsor) Can your architecture defend against attacks that are autonomous, adaptive, and faster than anything you've seen before? Frontier AI models are compressing the attack lifecycle and enabling a new generation of agentic threats. Security t
Last cached leaderboard date: May 25, 2026. This model page is generated from the AskClash LLM Leaderboard cache and linked from the live leaderboard.