Model comparison
GLM 5.3 vs Qwen3.8-2.4T-A95B
Z.ai against Qwen, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
Z.ai
GLM 5.3
Frontier
Qwen
Qwen3.8-2.4T-A95B
Frontier · Open weights
| Specification | GLM 5.3 | Qwen3.8-2.4T-A95B |
|---|---|---|
| Provider | Z.ai | Qwen |
| Tier | Frontier | Frontier |
| Context window | Winner: 1M | 262K |
| Max output | 128K | Winner: 131K |
| Input / 1M tokens | Not verified | Not verified |
| Output / 1M tokens | Not verified | Not verified |
| Weights | Closed | Open |
| Parameters | Not disclosed | 2.4T total / 95B active (MoE) |
| Reasoning levels | low, high, max | low, medium, xhigh |
| Modalities | text | text |
| Released | August 14, 2026 | August 12, 2026 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: GLM 5.3: Direct `glm-5.3` API is documented as coming soon and is not on the Z.ai per-token table (do not assume GLM 5.2's $1.40/$4.40). Today it is on every GLM Coding Plan: points-based quota (input / cached input / output). Off-peak is 50% of standard points. Peak is Monday–Friday 14:00–18:00 UTC+8. Coding Plan requests for GLM-5.2/5.1 are routed to 5.3.
GLM 5.3
Z.ai's 2026-08-14 Coding Plan flagship — same base as GLM 5.2, with the documented gains from post-training only. Direct token API and open weights are not published yet.
Best for
- GLM Coding Plan agent work
- Long-horizon coding in Claude Code / OpenCode / Cline
- Teams already on Z.ai subscriptions
Watch out
Thinking cannot be disabled (`thinking.type: disabled` fails); `reasoning_effort` is low / high / max (default max). Open weights are promised about two weeks after launch pending safety review — not a downloadable checkpoint today. Vendor DeepSWE v1.1 66.9 used mini-swe-agent at 400K context, not the public Datacurve v1.1 board (snapshot generated 2026-08-13, before this launch).
Qwen3.8-2.4T-A95B
Official Hugging Face checkpoint for Qwen3.8 open weights — a text base model, not the hosted Qwen3.8-Max API.
Best for
- Self-hosting the Qwen3.8 flagship
- vLLM / SGLang serving
- Evaluating the open Max-class weights
Watch out
Native context is 262,144 tokens (YaRN extends toward ~1,010,000). Hugging Face best-practice caps: 262,144 reasoning tokens and 131,072 final-response tokens. Thinking cannot be disabled. Qwen3.8-Max License: MaaS / AI Work Assistant businesses over $50M trailing 12-month revenue need a separate commercial licence. Self-hosting still needs datacentre-class memory. Do not treat this checkpoint as the multimodal 1M-context API.
Common questions
GLM 5.3 vs Qwen3.8-2.4T-A95B
Answered from the verified figures on this page rather than general guidance.
Which has the larger context window, GLM 5.3 or Qwen3.8-2.4T-A95B?
GLM 5.3 accepts 1M tokens against 262K for Qwen3.8-2.4T-A95B. This only matters if you routinely send very long documents or large codebases.
Do GLM 5.3 and Qwen3.8-2.4T-A95B support the same reasoning levels?
GLM 5.3 exposes low, high, max, while Qwen3.8-2.4T-A95B exposes low, medium, xhigh.
Should I use GLM 5.3 or Qwen3.8-2.4T-A95B?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.3 suits glm coding plan agent work; Qwen3.8-2.4T-A95B suits self-hosting the qwen3.8 flagship.
Can I self-host GLM 5.3 or Qwen3.8-2.4T-A95B?
Qwen3.8-2.4T-A95B publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GLM 5.3 is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.