AI tool comparison
GLM-5.1 vs Qwen3.6-35B-A3B
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
GLM-5.1
#1 on SWE-Bench Pro — Zhipu's open 754B MoE beats GPT-5 on coding
50%
Panel ship
—
Community
Paid
Entry
Z.ai (formerly Zhipu AI) has released GLM-5.1, a 754B-parameter Mixture-of-Experts model that's currently sitting at #1 on SWE-Bench Pro with a score of 58.4 — outperforming GPT-5.4 and Claude Opus 4.6 on long-horizon software engineering tasks. The model ships under MIT license with full weights on HuggingFace. GLM-5.1 was specifically designed for agentic software engineering workflows: multi-file reasoning, autonomous test-run-fix loops, and extended coding sessions that span hundreds of tool calls. It's not just a capability leap — at 754B active parameters via sparse MoE, it can be run more efficiently than a dense model of equivalent capability on a sufficiently provisioned cluster. The SWE-Bench Pro result is significant because that benchmark is harder to game than vanilla SWE-Bench Verified. It tests whether a model can resolve real GitHub issues with correct tests, proper diffs, and no regressions — the things that actually matter in production. For anyone running self-hosted coding agents or building on open models, GLM-5.1 just became the new baseline to beat.
Open Source Models
Qwen3.6-35B-A3B
35B total, 3B active: Alibaba's lean MoE coding beast goes fully open source
75%
Panel ship
—
Community
Free
Entry
Alibaba's Qwen team open-sourced Qwen3.6-35B-A3B on April 16, 2026 — a sparse Mixture-of-Experts model with 35 billion total parameters but only ~3 billion active per forward pass. That architectural trick is the whole story: you get near-frontier performance while consuming compute comparable to a 3B dense model. It's available under Apache 2.0 on Hugging Face and ModelScope. The model supports a 262K token context window (extensible to 1M with YaRN), multimodal inputs including text, images, and video, and is purpose-built for agentic coding workflows. On SWE-bench and Terminal-Bench it outperforms the much larger dense Qwen3.5-27B, matching Gemma4-31B on several benchmarks. RefCOCO visual grounding score hits 92.0 — some multimodal metrics reach Claude Sonnet 4.5 territory. Community reaction has been immediate: r/LocalLLaMA lit up with benchmarks showing it solving coding tasks that models with 10x the active parameters couldn't handle. The FP8 quantized variant runs comfortably on a single 24GB consumer GPU, making this the most capable locally-runnable coding agent most developers have ever had access to.
Reviewer scorecard
“If the SWE-Bench Pro numbers hold up under independent replication, this is the first open model that can genuinely replace a proprietary API for serious agentic coding work. MIT license means you can fine-tune and deploy on your own infra. This is a big deal.”
“3B active parameters with 35B parameter breadth is engineering magic. I'm getting near-frontier coding results in Cline and running it locally on a 3090 — the refusals are lower than Claude for security research too. Apache 2.0 means I can fine-tune it on my codebase. This is the best open-source coding model I've used.”
“754B parameters is not something 99% of developers can run locally. You need a multi-GPU cluster or serious cloud spend. The benchmark numbers are from Z.ai's own evaluations, and Zhipu has a history of optimistic benchmarking. Wait for independent replications.”
“MoE models have notoriously bad batching throughput — if you're serving this at scale, the economics don't work out. And Alibaba's track record on long-term model support and safety filtering is shakier than Google or Anthropic. It's impressive in isolation, but enterprise teams should pressure-test it before replacing frontier APIs.”
“A Chinese lab shipping an MIT-licensed model that tops global coding benchmarks is a watershed moment for open-source AI. The geopolitical implications are real — this is the model that makes US export controls look strategically shortsighted.”
“The gap between open and closed models is closing faster than anyone predicted. When a freely downloadable model matches Claude Sonnet on multimodal benchmarks, the frontier lab pricing power evaporates. Qwen3.6-35B-A3B is another milestone in the commoditization of intelligence — and commoditization always accelerates adoption.”
“Unless you're building coding tools or agent infrastructure, a 754B MoE model doesn't move the needle for creative applications. The energy and infra overhead for creative use cases doesn't pencil out versus smaller, cheaper models.”
“I don't often care about coding models, but this one handles image + video understanding for design briefs surprisingly well. I used it to analyze a competitor's UI and generate a full redesign spec. The 262K context means I can feed entire brand guidelines without chunking.”
Weekly AI Tool Verdicts
Get the next comparison in your inbox
New AI tools ship daily. We compare them before you waste an afternoon.