Qwen3.5 vs GLM-5.2: Which Open-Weight Model Wins in 2026

Open-weight models stopped being the "budget option" a while ago. In 2026, they're where a lot of the real frontier action is happening, and two releases have dominated the conversation: Alibaba's Qwen3.5 and Zhipu AI's GLM-5.2. Both are Mixture-of-Experts (MoE) models, both are free to download and self-host, and both post benchmark scores that go toe-to-toe with closed frontier models from OpenAI, Anthropic, and Google. But they were built to win different fights.

This guide breaks down how Qwen3.5 and GLM-5.2 actually compare — architecture, benchmarks, pricing, context window, and licensing — and ends with a straightforward answer to "which one should I actually use."

The Quick Verdict

• Choose GLM-5.2 if your priority is autonomous coding agents, long-horizon software engineering tasks, or you want the strongest open-weight score on the Artificial Analysis Intelligence Index.

• Choose Qwen3.5 if you need native multimodality, a full family of model sizes (from lightweight edge models to a 397B flagship), massive multilingual coverage, or fast interactive throughput at long context.

Neither model is a strict upgrade over the other — they're optimized for different jobs. Here's the detail behind that verdict.

Release Timeline and Positioning

Zhipu AI shipped GLM-5.2 in June 2026 as a follow-up to the widely-used GLM-5, itself released in February 2026. GLM-5.2 quickly became the highest-ranked open-weight model on the Artificial Analysis Intelligence Index v4.1, a composite benchmark spanning nine evaluations including GDPval-AA, Terminal-Bench, SciCode, and Humanity's Last Exam.

Alibaba's Qwen3.5 landed earlier in the year, built on the Qwen-Next architecture with Gated DeltaNet (GDN) layers. Unlike GLM's single flagship approach, Qwen3.5 shipped as an entire family — from a tiny 0.8B model up to a dense 27B and a 397B-A17B MoE flagship — with reasoning enabled by default across the lineup and native multimodal input baked in from the start.

That difference in philosophy — one flagship model built for depth, versus a full range built for breadth — shapes almost every comparison that follows.

Architecture: Two Different Bets

GLM-5.2 is a Mixture-of-Experts model in the 744-billion total parameter class, combining a few notable design choices:

• Multi-head Latent Attention (MLA), which compresses key-value pairs into a latent space to cut memory overhead during inference.

• DeepSeek Sparse Attention (DSA), dynamically selecting which tokens to attend to across its context window.

• Multi-token Prediction (MTP), using extra prediction layers to speed up decoding.

It's also notable for training infrastructure: GLM-5 (and its successors) were trained entirely on domestic Chinese hardware, a detail that matters for organizations navigating export-control constraints.

Qwen3.5, by contrast, leans into the Qwen-Next architecture with GDN layers, and comes in dramatically more shapes — dense models from 0.8B to 27B, and MoE variants from 35B-A3B up to the 397B-A17B flagship. Every size in the family supports native multimodal input and ships with reasoning turned on by default (it can be disabled in the chat template if you don't want the overhead). The tradeoff: smaller Qwen3.5 models have a tendency to "overthink" simple prompts when reasoning is left on.

Benchmark Comparison

Here's how the two models stack up, benchmark by benchmark:

• Artificial Analysis Intelligence Index v4.1: GLM-5.2 scores 51, the top result among all open-weight models. Qwen3.5 sits behind both GLM-5.2 and MiniMax M3 on this composite index.

• SWE-bench Verified (real-world software engineering): GLM-5.2 leads at 77.8%, with Qwen3.5 close behind at roughly 76.4% — only a 1.4-point gap.

• LiveCodeBench (isolated code generation): Qwen3.5 dominates here at 83.6%, far ahead of GLM-5.2's 52.0.

• MMLU-Pro (general knowledge): Qwen3.5 leads by a wide margin — about 17 points ahead of GLM-5.2.

• GPQA Diamond (graduate-level reasoning): Qwen3.5 leads at 88.4.

• Humanity's Last Exam, tool-augmented: GLM-5.2 posts the only reported score here at 50.4, beating even GPT-5.2's 45.5.

• Throughput at 256K context: Qwen3.5 decodes roughly 19x faster than the earlier Qwen3-Max, making it noticeably snappier in interactive sessions.

A few things stand out. GLM-5.2 is the strongest open-weight model on SWE-bench Verified, the benchmark that most closely approximates real end-to-end software engineering — fixing actual bugs in actual repos. That's a big deal for anyone building autonomous coding agents.

But Qwen3.5 crushes GLM on LiveCodeBench, which tests isolated code-generation problems rather than repo-level fixes. That split matters: if your use case is "write me this function," Qwen3.5 is arguably the stronger choice. If your use case is "go fix this GitHub issue autonomously," GLM-5.2 pulls ahead.

On knowledge and reasoning, Qwen3.5 leads MMLU-Pro by a wide 17-point margin and tops GPQA Diamond at 88.4. GLM-5.2 counters with the only reported tool-augmented Humanity's Last Exam score among the two, beating even GPT-5.2 on that specific test.

Context Window and Speed

GLM-5.2 ships with a stable 1-million-token context window, a jump up from GLM-5's 205K. That's a serious upgrade for anyone doing large-codebase analysis or long-document workflows.

Qwen3.5's standout isn't raw context — it's throughput. At 256K context, it decodes roughly 19x faster than the earlier Qwen3-Max, which makes it genuinely usable for interactive coding sessions where latency, not just intelligence, determines whether a tool feels good to use.

Pricing and Licensing

Both models are released under permissive open licenses (MIT for GLM-5.2), meaning you can download the weights and self-host with no usage restrictions — at which point per-token API costs and data-residency concerns both become non-issues.

On hosted API pricing, GLM-5.2's official list price sits around $1.40 / $4.40 per million tokens (input/output), though the open-weight nature of the model means third-party hosts compete on price, pushing the effective market median closer to $0.55 / $1.85. Qwen3.5's smallest variant (0.8B) is priced as low as $0.01 per million tokens (blended), making it one of the cheapest usable models on the market — though that's obviously not an apples-to-apples comparison against GLM's flagship-only pricing.

The real story either way: both models land within a fraction of what closed frontier labs charge. For context, Claude Opus 4.8 charges around $25 per million output tokens for its hardest coding tier — GLM-5.2 and Qwen3.5 are landing a few points behind that quality bar for somewhere between a quarter and a twentieth of the price.

Multimodality

This is one of the clearest differentiators. Qwen3.5 is natively multimodal across its entire family — every size, from the smallest dense model to the 397B flagship, accepts multimodal input. GLM-5.2 is text-only at the flagship tier (Zhipu's multimodal capability lives in separate model lines). If your application needs to look at images or video, Qwen3.5 is the only one of the two that does it out of the box.

Multilingual Coverage

Qwen3.5 also has a meaningful edge on multilingual breadth, with coverage reportedly extending to 200+ languages and noticeably improved instruction-following and style compared to the previous Qwen generation. GLM-5.2's multilingual performance is solid but hasn't been positioned as a headline differentiator the way Qwen's has.

Which One Should You Actually Use?

Reach for GLM-5.2 when:

• You're building autonomous coding agents or long-horizon software engineering workflows

• SWE-bench-style, repo-level bug fixing is your core use case

• You want the highest-ranked open-weight score on the Artificial Analysis Intelligence Index

• A 1M-token context window matters for large-codebase or long-document tasks

Reach for Qwen3.5 when:

• You need multimodal input (images, and beyond) baked into the model natively

• You want a full family of sizes to match different deployment footprints, from edge to flagship

• Isolated code generation, general knowledge, or reasoning benchmarks (MMLU-Pro, GPQA Diamond) matter more than repo-level coding

• Interactive latency at long context is a priority

• You need broad multilingual coverage

The Bigger Picture

The gap between open-weight and closed frontier models has effectively closed on most standardized benchmarks in 2026. What's left to differentiate closed models is polish, safety tuning, and ecosystem maturity — not raw capability. Both Qwen3.5 and GLM-5.2 are proof of that: two models, two different bets, both landing within striking distance of the best proprietary systems on the market, at a fraction of the cost.

The honest takeaway isn't "Qwen beats GLM" or the reverse — it's that the right pick depends entirely on the job. Coding agents lean GLM. Multimodal, multilingual, and general-purpose deployments lean Qwen. And if your workload spans both, running them side by side and routing by task is increasingly a realistic option given how cheap both have become.

Benchmarks and pricing move fast in this space — always confirm current numbers against official model cards and vendor pricing pages before making a production decision.