Qwen3.5 vs GPT-5.2 vs Claude 4.5: Benchmark Comparison and Performance Analysis

Mar 24, 2026

Organizations want to run LLMs locally for privacy, cost, or latency reasons. But they need to know if open-source alternatives can match closed-source model quality. I analyzed the benchmark data comparing Qwen3.5-397B against GPT-5.2 and Claude 4.5 Opus to answer this question.

The Core Question

Can an open-source model like Qwen3.5 compete with the leading closed-source models from OpenAI and Anthropic?

The answer surprised me. In several key benchmarks, Qwen3.5 doesn’t just compete—it wins. But there are trade-offs you need to understand before making a deployment decision.

Overall Benchmark Comparison

Here’s the head-to-head comparison across the most important benchmarks:

| Benchmark        | Qwen3.5-397B | GPT-5.2 | Claude 4.5 Opus |
|------------------|-------------|---------|-----------------|
| MMLU-Pro         | 87.8        | 87.4    | 89.5            |
| BrowseComp       | 78.6        | 65.8    | 67.8            |
| IFBench          | 76.5        | 75.4    | 58.0            |
| MultiChallenge   | 67.6        | 64.2    | 61.3            |
| AIME26 (Math)    | 91.3        | 96.7    | 93.3            |
| SWE-bench        | 76.4        | 80.0    | 80.9            |

The data tells an interesting story. Qwen3.5 leads in three categories, trails in two, and ties in one. But the margins matter more than the wins.

Where Qwen3.5 Wins

Search and Web Browsing (BrowseComp)

Qwen3.5 scores 78.6 on BrowseComp, beating GPT-5.2 by 19.5% and Claude 4.5 by 15.9%. This benchmark tests how well models can search, browse, and extract information from the web.

If you’re building:

Research agents
Knowledge extraction systems
Automated data collection tools
Web scraping with reasoning

Qwen3.5 is the clear winner. The gap is significant enough that I’d recommend it even if other benchmarks were equal.

Instruction Following (IFBench)

Qwen3.5 scores 76.5 on IFBench, slightly ahead of GPT-5.2 (75.4) and far ahead of Claude 4.5 (58.0).

This matters for:

Complex prompt engineering
Multi-step task execution
Following detailed formatting requirements
Agent workflows with specific output needs

I find this particularly important for production systems. Models that follow instructions precisely reduce the need for post-processing and error handling.

Multi-Task Coordination (MultiChallenge)

Qwen3.5 leads with 67.6, ahead of GPT-5.2 (64.2) and Claude 4.5 (61.3).

MultiChallenge tests how well models handle:

Multiple simultaneous requirements
Conflicting instructions
Complex, layered tasks

This benchmark reflects real-world complexity. Most production workloads involve multiple constraints and requirements simultaneously.

Where Qwen3.5 Trails

Mathematical Reasoning (AIME26)

| Model             | AIME26 Score | Gap to Leader |
|-------------------|--------------|---------------|
| GPT-5.2           | 96.7         | -             |
| Claude 4.5 Opus   | 93.3         | -3.4          |
| Qwen3.5-397B      | 91.3         | -5.4          |

Qwen3.5 trails GPT-5.2 by 5.4 points on mathematical reasoning. This matters for:

Scientific computing applications
Algorithm design requiring math proofs
Financial modeling
Engineering calculations

For most applications, a 5.4-point gap is noticeable but not critical. If math-heavy tasks are your primary use case, GPT-5.2 has a real advantage.

Code Generation (SWE-bench Verified)

| Model             | SWE-bench Verified | Gap to Leader |
|-------------------|-------------------|---------------|
| Claude 4.5 Opus   | 80.9              | -             |
| GPT-5.2           | 80.0              | -0.9          |
| Qwen3.5-397B      | 76.4              | -4.5          |

Qwen3.5 trails Claude 4.5 by 4.5 points on SWE-bench. This benchmark tests:

Bug fixing in real repositories
Code modification across multiple files
Understanding existing codebases

The gap here is meaningful. For code-heavy workloads, the closed-source models maintain an edge. However, 76.4 is still a strong score for an open-source model.

The MMLU-Pro Tie

MMLU-Pro tests general knowledge and reasoning across diverse subjects:

| Model             | MMLU-Pro Score |
|-------------------|----------------|
| Claude 4.5 Opus   | 89.5           |
| Qwen3.5-397B      | 87.8           |
| GPT-5.2           | 87.4           |

Qwen3.5 essentially ties with GPT-5.2 and is only 1.7 points behind Claude. For general-purpose applications, this shows Qwen3.5 is competitive with the best closed-source models.

Why Local Deployment Matters

I think the benchmark data becomes more meaningful when you consider the advantages of local deployment:

Privacy: Your data never leaves your infrastructure. This matters for:

Healthcare applications (HIPAA)
Financial services (regulatory compliance)
Legal documents (attorney-client privilege)
Proprietary research (trade secrets)

Cost: No per-token API charges. For high-volume applications:

Customer service chatbots
Internal documentation systems
Research and analysis tools
Batch processing workloads

Latency: No network round-trips. Critical for:

Real-time applications
Edge deployments
Offline scenarios
High-frequency interactions

Control: Full control over model behavior:

No API changes breaking your application
No rate limits or usage restrictions
Custom fine-tuning options
Predictable performance

The Decision Matrix

I created this matrix to help you decide:

| Your Priority          | Choose Qwen3.5 If...              | Choose Closed-Source If...    |
|------------------------|-----------------------------------|-------------------------------|
| Privacy                | Data cannot leave your servers    | Compliance allows cloud APIs  |
| Cost                   | High volume, predictable budget  | Low/variable usage            |
| Latency                | Real-time requirements            | API latency acceptable        |
| Search/Browse Tasks    | Primary use case                  | Occasional use                |
| Math Reasoning         | General math is sufficient        | Advanced math critical        |
| Code Generation        | Good enough quality needed       | Best quality required         |
| Instruction Following  | Complex prompts common            | Simple prompts sufficient     |

The 256K Context Window

One advantage I haven’t mentioned: Qwen3.5 offers a 256K context window. This matters for:

Processing long documents
Maintaining conversation history
Analyzing entire codebases
Multi-document reasoning

For many applications, this extended context window compensates for the smaller gaps in math and code benchmarks.

Common Mistakes to Avoid

When evaluating these benchmarks, I see developers make several mistakes:

Mistake 1: Assuming bigger closed-source models are always better

The data shows Qwen3.5 wins in specific categories. Match the model to your use case, not the brand name.

Mistake 2: Ignoring benchmark relevance to your use case

If you’re building a search agent, BrowseComp matters more than AIME26. If you’re building a coding assistant, SWE-bench matters more than BrowseComp.

Mistake 3: Not considering the context window

A 256K context window changes what you can do with a model. Don’t just compare benchmark scores—consider practical capabilities.

Mistake 4: Overlooking deployment costs

API costs add up. A locally-deployed model that’s 5% worse but 90% cheaper might be the right choice for your budget.

Based on the benchmark analysis, here’s my recommendation framework:

Choose Qwen3.5-397B if:

Privacy or compliance requires on-premise deployment
You need strong search and browsing capabilities
Instruction following is critical
Cost control is a priority
You need a 256K context window

Choose GPT-5.2 or Claude 4.5 if:

Mathematical reasoning is your primary need
Best-in-class code generation matters
You prefer managed infrastructure
Your usage is low enough that API costs are manageable

Consider a hybrid approach:

Use Qwen3.5 for search-heavy, instruction-following tasks
Use GPT-5.2 or Claude for math-intensive or code-intensive tasks
Route requests based on task type

The Bigger Picture

I think Qwen3.5 represents a significant milestone. Open-source models have caught up to the point where the decision is no longer “open-source vs. quality” but rather “which trade-offs fit my use case.”

For organizations prioritizing local deployment, Qwen3.5 offers a viable open-source alternative without the API costs or data privacy concerns. The gaps in math and code are small enough that, for most production workloads, the other advantages outweigh them.

The key is matching the model to your specific needs rather than chasing the highest benchmark scores across all categories.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Qwen3.5 Official Release

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!