GLM 5 vs Qwen3 235B A22B (2507)

Z.ai · China | Alibaba · China · Updated June 2026

Quick verdict

Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B (2507) is the value pick.

GLM 5 (Z.ai) and Qwen3 235B A22B (2507) (Alibaba) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

▸Context window: Qwen3 235B A22B (2507) holds 1.3× more — 256K (~393 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: GLM 5 is the newer model by about 7 months (released February 12, 2026), usually meaning fresher training data and capabilities.

Side-by-side specs

Spec	GLM 5	Qwen3 235B A22B (2507)
Provider	Z.ai (China)	Alibaba (China)
Released	February 12, 2026	July 21, 2025
Context window	200K (~300 pages)	256K (~393 pages)
Price (in/out)	$1/$3.2 per 1M tokens	Open weight (self-host / free)
Open weight?	Yes — self-hostable	Yes — self-hostable
Modalities	text, code	text, code
SWE-Bench Verified	77.8%	Not published
MRCR v2 @ 1M	Not published	Not published

Who wins what

Agentic planning and long-horizon coding workflows

GLM 5

Qwen3 235B A22B (2507) is comparatively weak here — coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on

Complex systems design and backend reasoning

GLM 5

Qwen3 235B A22B (2507) is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning

Iterative self-correction on autonomous tasks

GLM 5

Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it is the newer of the two.

Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)

Qwen3 235B A22B (2507)

An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it carries the larger 256K context.

Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)

Qwen3 235B A22B (2507)

Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; GLM 5 does not.

Outstanding structured logic — 95.0 on ZebraLogic

Qwen3 235B A22B (2507)

Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; GLM 5 does not.

Lowest cost at scale

Qwen3 235B A22B (2507)

Its weights are open, so at volume you pay for your own hardware instead of GLM 5's $1/$3.2 per 1M tokens.

Largest single-prompt input

Qwen3 235B A22B (2507)

Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Qwen3 235B A22B (2507)

At Open weight (self-host / free) it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Qwen3 235B A22B (2507)

Larger 256K window fits more in one prompt.

Anyone whose priority is agentic planning and long-horizon coding workflows

→ GLM 5

It is specifically built for that.

Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)

→ Qwen3 235B A22B (2507)

That is its strongest area.

GLM 5: where it fits

Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.

Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.

Qwen3 235B A22B (2507): where it fits

An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.

Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

GLM 5 and Qwen3 235B A22B (2507) overlap enough that the right pick depends on your specific job. Qwen3 235B A22B (2507) costs less per token; Qwen3 235B A22B (2507) holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.

Want both GLM 5 and Qwen3 235B A22B (2507) without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is GLM 5 or Qwen3 235B A22B (2507) better for coding?

Public SWE-Bench figures are not available for Qwen3 235B A22B (2507), so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows while Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GLM 5 or Qwen3 235B A22B (2507)?

Qwen3 235B A22B (2507) is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

Qwen3 235B A22B (2507) — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GLM 5 and Qwen3 235B A22B (2507) together?

Yes — a multi-model platform like LumiChats gives you GLM 5, Qwen3 235B A22B (2507) and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, GLM 5 or Qwen3 235B A22B (2507)?

GLM 5 — released February 12, 2026, about 7 months after Qwen3 235B A22B (2507).

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Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.