LLM Reference

Qwen3.5-35B-A3B

Released
2026-02-24
Last refreshed
2026-06-29
Status
Researched 131d ago
Open sourceCommercial use: permittedCodingRAGAgentsLong contextClassificationJSON / Tool use

Qwen3.5-35B-A3B is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 262k context window
  • Buyers comparing 2 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
Qwen3.5
Released
2026-02-24
Context
262k
Parameters
35B
Architecture
Mixture of Experts
Openness
Open source
License
Apache 2.0OSI-approvedCommercial use: permitted
Weights
Available
Code
Unknown
Created by

AI research institute of Alibaba Group.

Hangzhou, Zhejiang, China
Founded 2017
Website
Pricing
Output / 1M
$1.00
Input / 1M
$0.139

Cheapest of 2 routes · OpenRouter

About

Alibaba's Qwen3.5-35B-A3B is a Mixture-of-Experts model released February 24, 2026, with 35B total parameters and 3B active during inference. Part of the Qwen3.5 series with a 262K native context window (extendable to ~1M tokens). Optimized for high inference throughput (78+ tokens/second on NVIDIA hardware). Open-source under Apache 2.0.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ C

2 relevant benchmarks in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Q/$ B

3 relevant benchmarks in the decision map.

Provider price ladder

Compare all 2

Compare API pricing across 2 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
OpenRouter$0.139$1.00
Serverless
Novita AI$0.250$2.00
Serverless

Capabilities

ReasoningJSON / Tool useStructured Outputs

Benchmark peer barsfor Coding

Benchmark scores(9)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionEvaluationSource
Google-Proof Q&A84.5diamondObserved 2026-04-18Source
MMMU Pro75.1LLM-Stats aggregatorObserved 2026-06-07Source
SWE-rebench53.7pass@1 (best of 5 runs)Observed 2026-05-28Source
Berkeley Function Calling Leaderboard v367.3BFCL-V4, from official model card (accuracy)Observed 2026-06-07Source
Humanity's Last Exam22.4HLE with CoT, no tools, from official model card (accuracy)Observed 2026-06-07Source
LiveCodeBench74.6LiveCodeBench v6 (pass@1)Observed 2026-06-07Source
MMLU PRO85.3From official HuggingFace model card (accuracy)Observed 2026-06-07Source
SWE-bench Verified69.2From official model card (resolved)Observed 2026-06-07Source
τ-bench81.2TAU2-Bench, from official model card (accuracy)Observed 2026-06-07Source

Migration checks

No linked migration route is available for this model yet.

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