Qwen 3.6 35B Coding Benchmarks: SWE-bench, LiveCode Results
Qwen 3.6 35B-A3B achieves 73.4 on SWE-bench Verified and 80.4 on LiveCodeBench v6. Compare benchmarks and deploy via Tessera AI.
Qwen 3.6 35B Coding Benchmarks: SWE-bench, LiveCode Results
Qwen 3.6 35B-A3B scores 73.4 on SWE-bench Verified, 51.5 on Terminal-Bench 2.0, and 80.4 on LiveCodeBench v6. These results represent a meaningful jump over the prior Qwen 3.5 35B-A3B release and confirm the model as one of the strongest mid-sized open-weight options for agentic coding workloads. Released by Alibaba on April 16, 2026 under the Apache 2.0 license, it pairs frontier-level software engineering scores with a sparse architecture that keeps inference cheap.
This guide walks through every published coding score, explains what each benchmark actually measures, and compares the 35B model directly against its same-size predecessor so you can judge whether the upgrade is worth it. It also flags where the official numbers and community results diverge, so you do not over-read any single benchmark in isolation.
Qwen 3.6 35B-A3B coding benchmark scores
Alibaba lists the headline coding numbers in its official model card on Hugging Face. The table below shows the Qwen 3.6 35B-A3B results next to the Qwen 3.5 35B-A3B baseline so the generation-over-generation gain is clear.
| Benchmark | Qwen 3.6 35B-A3B | Qwen 3.5 35B-A3B |
|---|---|---|
| SWE-bench Verified | 73.4 | 70.0 |
| Terminal-Bench 2.0 | 51.5 | 40.5 |
| LiveCodeBench v6 | 80.4 | 74.6 |
| MCPMark | 37.0 | 27.0 |
| GPQA Diamond | 86.0 | 84.2 |
| AIME 2026 | 92.7 | 91.0 |
The largest coding gains land on the agentic benchmarks. Terminal-Bench 2.0 rises by 11 points and MCPMark by 10 points, both of which reward multi-step tool use rather than single-shot code generation. SWE-bench Verified, the most cited real-world software engineering benchmark, climbs 3.4 points to 73.4.
Early third-party coverage reports additional SWE-bench variants for the model, including roughly 67.2 on SWE-bench Multilingual and 49.5 on SWE-bench Pro, per a detailed review on DEV Community. Treat those secondary figures as community-reported until Alibaba publishes the full extended table.
What each coding benchmark measures
Benchmark numbers only help if you know what task they stand for. Here is what the headline scores test.
- SWE-bench Verified asks the model to resolve real GitHub issues from open-source Python projects, with a human-verified subset that filters out broken or ambiguous tasks. A score of 73.4 means the model produced a patch that passed the hidden test suite on roughly three of every four issues.
- Terminal-Bench 2.0 measures how well a model drives a real shell to complete end-to-end tasks: editing files, running commands, and reacting to output. It rewards planning and recovery from errors, which is why the 51.5 result matters more for agent builders than a raw code-completion score.
- LiveCodeBench v6 uses competitive-programming problems published after the model’s training cutoff, so it resists contamination from memorized solutions. The 80.4 result points to genuine reasoning on novel problems.
- MCPMark evaluates tool use through the Model Context Protocol, the same interface many agent frameworks now use to expose tools to a model.
Standard one-shot generation tests such as older HumanEval and MBPP variants show the model roughly on par with the previous Qwen generation, as early community reviews note. The real step change is in agentic, multi-turn coding, not in single-function completion.
Qwen 3.6 35B vs Qwen 3.5 35B, same size
The cleanest way to read this release is to hold model size constant and compare 35B-A3B against 35B-A3B. On that basis Qwen 3.6 improves on every coding benchmark in the table above, with the steepest gains on the agentic suites. For a deeper side-by-side across reasoning and knowledge tasks too, see our Qwen 3.6 vs 3.5 performance comparison.
A widely shared Hacker News thread captures the nuance from hands-on use: on one community “Power Ranking” coding suite, Qwen 3.6 35B-A3B solved 11 of 98 tasks (best-of-two) versus 10 of 98 for the same-size Qwen 3.5. That is a small absolute gap on a hard private benchmark, a useful reminder that the official suites and difficult real-world task sets can tell slightly different stories.
The takeaway: the generation-over-generation gain is real and consistent on the public agentic benchmarks, while the hardest bespoke task sets show a narrower edge. For most teams evaluating an upgrade, the published SWE-bench and Terminal-Bench gains are the figures that translate to day-to-day coding agents. Our Qwen 3.6 35B benchmark hub tracks the full picture across coding, reasoning, and knowledge.
How agentic harnesses change the numbers
Coding scores for open models depend heavily on the harness wrapped around them. Community testing on r/LocalLLaMA reports that pairing Qwen 3.6 35B-A3B with a purpose-built agent loop pushes it into the public Polyglot top 10 at around a 78.7% success rate, competitive with much larger cloud models on that test.
The practical lesson is that benchmark figures published with a strong scaffold will not match a naive single-call setup. If you plan to run the model as a coding agent, budget time to tune the harness: tool definitions, retry logic, and context management move the real-world success rate as much as the model version does.
Architecture and context window
Qwen 3.6 35B-A3B is a mixture-of-experts model with 35B total parameters and roughly 3B active parameters per token. The sparse routing activates only the expert layers a given prompt needs, which is what lets a 35B-class model deliver the inference cost and latency closer to a 3B dense model. That efficiency is the reason it runs on consumer hardware locally and stays inexpensive to serve through an API.
The model ships with a 262,144-token native context window, extendable to around one million tokens with YaRN scaling. For coding agents that load large repositories, long diffs, and extended tool transcripts, that headroom removes a common failure mode where the agent loses earlier context mid-task. Architecture and license details are on the Hugging Face model card; the model is Apache 2.0, so it can be self-hosted or served through a managed provider without restrictive terms.
Hardware to run Qwen 3.6 35B-A3B locally
Because only 3B parameters are active per token, Qwen 3.6 35B-A3B is far more forgiving to run locally than its 35B total size suggests. Community testing reports that a quantized GGUF build runs on a 24GB setup, including Apple Silicon Macs, per the Build Fast with AI review. Lower-bit quantization trades some accuracy for a smaller memory footprint, so the coding scores above assume a high-quality or full-precision deployment rather than an aggressive quant.
For teams comparing local versus API, the decision usually comes down to three factors: sustained throughput under concurrency, the operational cost of maintaining GPUs and an agent runtime, and whether inference must stay in a specific region for compliance. A single developer experimenting on a laptop is well served by a local GGUF build. A production coding agent handling many concurrent sessions, long context windows, and uptime guarantees is usually cheaper and simpler to run through a managed endpoint, where the provider absorbs the GPU and scaling work.
Run Qwen 3.6 35B-A3B on Tessera
If you would rather not manage GPUs, quantization, and an agent runtime yourself, Tessera serves Qwen 3.6 35B-A3B through a flat monthly subscription with an OpenAI-compatible API. You point your existing client at a new base URL and keep your tooling. Tessera serves open-source models only, with EU and LATAM data residency for teams that need their inference to stay in-region.
Pricing is flat rather than per-token, so a coding agent that bursts through long context windows does not produce a surprise invoice at the end of the month. You can see the model list on our available models page and the cost structure on private AI pricing. For agentic coding workloads, predictable cost on a long-context model is often the difference between a prototype and something you can leave running in production.
FAQ
How does Qwen 3.6 35B compare to Qwen 3.5 35B?
Qwen 3.6 35B-A3B improves on the same-size Qwen 3.5 across every published coding benchmark, including SWE-bench Verified from 70.0 to 73.4, Terminal-Bench 2.0 from 40.5 to 51.5, and LiveCodeBench v6 from 74.6 to 80.4. The biggest gains are on agentic, multi-step benchmarks.
What is the context window for Qwen 3.6 35B?
The model supports a 262,144-token native context window, extendable to roughly one million tokens with YaRN scaling.
Is Qwen 3.6 35B-A3B a dense or MoE model?
It is a mixture-of-experts model with 35B total parameters and about 3B active parameters per token, which keeps inference cost and latency close to a much smaller dense model.
Do these benchmark scores hold up in real coding agents?
The official SWE-bench and Terminal-Bench gains are consistent and reproducible, but real-world success also depends on the agent harness. Community tests show the same model swinging widely in success rate depending on the scaffold, retry logic, and context management around it.
Where can I deploy Qwen 3.6 35B?
You can self-host it under Apache 2.0, or run it through Tessera with a flat monthly subscription, an OpenAI-compatible API, and EU or LATAM data residency.