Qwen3.5-27B vs Mistral Small 3.2: Apache 2.0 Heavyweights Go Head to Head

This is the comparison that matters most for teams choosing an open-weight model to self-host. Qwen3.5-27B and Mistral Small 3.2 are both dense models in the 24-27B parameter range, both ship under Apache 2.0, both support vision, and both target the same deployment hardware. On paper, they are the closest competitors in the open-weight space right now. In practice, the benchmark gap tells a different story.

Qwen posts MMLU-Pro 86.1 versus Mistral's 69.1. On GPQA Diamond - graduate-level scientific reasoning - Qwen scores 85.5 versus Mistral's 46.1. That is a 39-point spread. On SWE-bench Verified, Qwen delivers 72.4 while Mistral has no reported score. These numbers suggest Qwen is operating in a different capability tier on reasoning and coding tasks. But Mistral has its own advantages: mature function calling, EU compliance positioning, established enterprise relationships, and a production-hardened deployment story that Qwen's newer architecture has not yet matched.

Quick Comparison

Qwen3.5-27B: The Benchmark Leader

Qwen3.5-27B is the dense model in Alibaba's February 2026 Qwen 3.5 family. At 27B parameters, it sits between the lighter Qwen3.5-35B-A3B MoE model (which activates only 3B per token) and the mid-range Qwen3.5-122B-A10B. But unlike those MoE siblings, this is a dense model - every parameter activates on every forward pass - which means the 27B count represents both total and active parameters.

The architecture is a hybrid of Gated DeltaNet and Gated Attention in a 3:1 ratio across 64 layers. The DeltaNet layers use linear attention with 48 heads, while the full attention layers use 24 query heads and 4 KV heads with a head dimension of 256. This hybrid approach gives the model O(n) scaling on three-quarters of its layers while maintaining full quadratic attention quality on the remaining quarter. The practical result is efficient long-context processing without sacrificing the attention patterns that reasoning tasks demand.

The benchmark numbers are the story here. MMLU-Pro at 86.1 places Qwen ahead of most 70B models from the previous generation. GPQA Diamond at 85.5 is competitive with frontier proprietary models on graduate-level science. SWE-bench Verified at 72.4 means the model can fix nearly three out of four real GitHub issues. LiveCodeBench v6 at 80.7 demonstrates competitive programming capability. IFEval at 95.0 shows strong instruction following. These scores are not in the same neighborhood as Mistral Small 3.2 - they are in a different city.

The context window of 262K tokens, extendable past 1 million with YaRN scaling, is double Mistral's 128K. For workloads involving large codebases, long documents, or extended agent loops, this headroom matters. The model also supports native multimodal input including video - a capability Mistral does not offer. For an overview of how Qwen3.5 models compare to their Qwen 3 predecessors, see our Qwen 3 review.

Mistral Small 3.2: The Production Workhorse

Mistral Small 3.2 dropped in June 2025 as a surgical update to the Small 3.1 base. Mistral explicitly stated that this release focused on behavioral improvements - better instruction following, reduced repetition, stronger function calling - rather than new capabilities or architecture changes. That design philosophy tells you exactly who this model is for: teams that are already running Mistral in production and need reliability more than benchmark headlines.

The numbers back this up. Instruction accuracy improved from 82.75% to 84.78% on internal metrics. Infinite generation instances - a particularly annoying production bug where the model gets stuck in a loop - dropped from 2.11% to 1.29%. Arena Hard v2 nearly doubled from 19.56% to 43.1%. WildBench v2 jumped from 55.6% to 65.33%. These are the metrics that matter when you are serving millions of API requests and need predictable behavior. On the harder academic benchmarks, the improvements were more modest: MMLU-Pro moved from 66.76% to 69.06%, and GPQA Diamond inched from 45.96% to 46.13%.

Function calling is where Mistral genuinely competes. HumanEval Plus at 92.9% (Pass@5) is an excellent score for tool-use scenarios. If your application is an agent that needs to reliably parse function schemas, generate correct API calls, and handle structured output, Mistral has put significant engineering effort into this exact use case. The model also supports JSON mode output natively, which reduces parsing friction in production pipelines.

The EU compliance angle is worth noting. Mistral AI is a French company, subject to European data protection regulations, and has built its enterprise positioning around this. For organizations in regulated industries - finance, healthcare, government - where data sovereignty and vendor jurisdiction matter, Mistral's European base is a genuine differentiator that no amount of benchmark points from an Alibaba-backed model can replicate. The 128K context window, while half of Qwen's, is sufficient for most production workloads including multi-turn conversations, document analysis, and standard coding tasks.

Detailed Benchmark Comparison

N/R = Not Reported by model creator on the same evaluation protocol.

The pattern is clear: Qwen dominates on reasoning depth and coding capability by enormous margins. The GPQA gap of 39.4 points is the single most important number in this table - it measures whether the model can handle graduate-level scientific reasoning, and a gap that large means these models are not in the same tier for research-grade or complex analytical tasks.

Mistral holds its own in practical deployment metrics. HumanEval Plus at 92.9% (Pass@5) is excellent for function calling workflows. DocVQA at 94.9% shows strong document understanding. The WildBench and Arena Hard improvements from 3.1 to 3.2 demonstrate that Mistral is investing in the instruction-following quality that matters for production chatbots.

As always, these are self-reported benchmarks using each company's own evaluation harness. Check the Open Source LLM Leaderboard for independently verified scores and the Coding Benchmarks Leaderboard for standardized coding evaluations.

Pricing and Deployment

Self-hosting requirements are nearly identical. Both fit on a single A100 80GB or H100 in BF16, and both quantize down to consumer GPU territory at Q4. Mistral's 24B is slightly lighter than Qwen's 27B, which gives it a marginal edge on memory and throughput - but we are talking about a 12% parameter difference, not a category change.

On managed API pricing, Mistral offers a direct first-party API at $0.06/M input and $0.18/M output, which is competitively priced for the parameter count. Qwen's managed offering is primarily through Alibaba Cloud or the Qwen3.5-Flash hosted endpoint. Both models are available through third-party providers like DeepInfra, Together AI, and others.

Framework support favors Mistral slightly, primarily because the standard transformer architecture has broader tooling support than Qwen's Gated DeltaNet hybrid. Mistral has been available in production inference stacks since 2025 - the deployment patterns are well-understood. Qwen's DeltaNet layers require newer framework versions, and kernel optimization is still catching up. If you are deploying to production this month, Mistral has less deployment friction.

Both models ship under Apache 2.0, so licensing is a non-factor in this comparison. For context on how Apache 2.0 compares to other open-weight licenses, see our open source vs proprietary AI guide.

Pros and Cons

Qwen3.5-27B

Pros:

• MMLU-Pro 86.1, GPQA Diamond 85.5 - dramatically higher reasoning scores than Mistral
• SWE-bench Verified 72.4 - strong automated code repair capability
• LiveCodeBench v6 80.7 - competitive programming capability Mistral does not report
• 262K context window extendable to 1M+ - double Mistral's capacity
• Native multimodal including video understanding
• Gated DeltaNet architecture provides efficient long-context scaling
• IFEval 95.0 - strong instruction following

Cons:

• Newer architecture means less mature framework and deployment support
• February 2026 release - less community testing than Mistral's year-old ecosystem
• Self-reported benchmarks pending independent verification
• Thinking mode enabled by default increases output token usage
• Less established enterprise sales and support infrastructure
• No direct European data sovereignty positioning

Mistral Small 3.2

Pros:

• HumanEval Plus 92.9% (Pass@5) - excellent function calling and tool use
• DocVQA 94.9% - strong document understanding
• 1.29% infinite generation rate - production-grade reliability
• EU-based company with data sovereignty advantages for regulated industries
• Mature deployment ecosystem with well-understood inference patterns
• Direct first-party API at competitive pricing ($0.06/M input)
• Surgical improvement philosophy - predictable, incremental updates

Cons:

• MMLU-Pro 69.1 trails Qwen by 17 points on broad knowledge and reasoning
• GPQA Diamond 46.1 is 39 points behind - not competitive on hard reasoning tasks
• No SWE-bench or LiveCodeBench scores reported
• 128K context window is half of Qwen's capacity
• No video understanding capability
• MATH 69.4 versus Qwen's HMMT 92.0 - significant gap on mathematical reasoning

Verdict

The benchmark gap is too large to ignore. On the metrics that measure deep reasoning and coding capability, Qwen3.5-27B is not marginally better - it is a generation ahead. But production deployment is not a benchmark contest, and Mistral has real strengths that matter when uptime, compliance, and tool reliability are your primary concerns.

Choose Qwen3.5-27B if your workload demands reasoning depth. Coding agents, research assistants, complex analysis pipelines, mathematical reasoning - anything where the model needs to think hard rather than just respond quickly. The 39-point GPQA gap and the 72.4 SWE-bench score are not abstract - they translate directly into fewer failed attempts, less human review, and more reliable automated workflows. The 262K context window adds further separation for long-document and large-codebase tasks.

Choose Mistral Small 3.2 if you are building production systems where function calling reliability, EU compliance, and deployment maturity matter more than peak reasoning capability. Chatbots, customer support agents, document processing pipelines, and structured-output workflows - these are Mistral's sweet spot. The 92.9% HumanEval Plus score, the 94.9% DocVQA, and the halved infinite generation rate are exactly the metrics that production engineers care about. If your team is already on Mistral's API and your workload does not require solving graduate-level physics problems, switching to Qwen may not justify the migration cost.

Choose either if you need an Apache 2.0 licensed model in the 24-27B range for general-purpose tasks and have no strong ecosystem or compliance requirements. Both are commercially deployable, both fit on the same hardware, and both handle standard chat, summarization, and moderate coding tasks competently. But if forced to pick one with no constraints, the data points to Qwen3.5-27B as the stronger general-purpose model by a significant margin.

Sources:

• Qwen3.5-27B Model Card (HuggingFace)
• Qwen3.5: Towards Native Multimodal Agents (Blog)
• Mistral Small 3.2 24B Instruct Model Card (HuggingFace)
• Mistral Small 3.2 Release (VentureBeat)
• Mistral Small 3.2 Benchmarks (MarkTechPost)
• Mistral Small 3.2 Performance Analysis (Artificial Analysis)
• Qwen3.5 Features, Access, and Benchmarks (DataCamp)