Model comparison
GLM 5.2 vs Qwen3.8-Max
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 5.2
Frontier · Open weights
Qwen
Qwen3.8-Max
Frontier
| Specification | GLM 5.2 | Qwen3.8-Max |
|---|---|---|
| Provider | Z.ai | Qwen |
| Tier | Frontier | Frontier |
| Context window | 1M | 1M |
| Max output | 128K | Winner: 131K |
| Input / 1M tokens | Winner: $1.40 | $2 |
| Output / 1M tokens | Winner: $4.40 | $6 |
| Weights | Open | Closed |
| Parameters | Not disclosed | 2.4T total / ~95B active (MoE) |
| Reasoning levels | high, max | Not verified |
| Modalities | text | text, image, video |
| Released | June 16, 2026 | August 3, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | 53 | Winner: 58 |
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.
GLM 5.2
Z.ai's flagship GLM-5.2 coding model with a documented 1M-token context and 128K max output.
Best for
- Open-weight deployments
- Long-horizon coding agents
- Long context on a budget
Watch out
Ecosystem tooling is thinner than the closed frontier labs — budget integration time. Parameter count is not published on the current Z.ai model card, so it stays unverified here. GLM Coding Plan now defaults to GLM 5.3; requests for 5.2/5.1 on that plan are routed to 5.3. This row remains the open-weight / token-list API identity at $1.40/$4.40.
Qwen3.8-Max
Alibaba's hosted Max-class API — 2.4T MoE with a 1M context window (max input 991,808), vision, and managed tools. Distinct from the Hugging Face Qwen3.8-2.4T-A95B checkpoint.
Best for
- Agentic coding
- Multimodal knowledge work
- Long-context delivery
Watch out
This row is the hosted Qwen3.8-Max API, not the downloadable checkpoint. The open-weight Qwen3.8-2.4T-A95B card is catalogued separately and does not include the API's vision, non-thinking, or built-in tool extras.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
GLM 5.2 vs Qwen3.8-Max
Answered from the verified figures on this page rather than general guidance.
Is GLM 5.2 or Qwen3.8-Max cheaper for input?
GLM 5.2 is cheaper at $1.40 per million input tokens, against $2 for Qwen3.8-Max — roughly 1.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate.
Is GLM 5.2 or Qwen3.8-Max cheaper for output?
GLM 5.2 is cheaper at $4.40 per million output tokens, against $6 for Qwen3.8-Max — roughly 1.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate.
Which has the larger context window, GLM 5.2 or Qwen3.8-Max?
Both accept about 1M tokens of context, so document length will not decide between them.
Should I use GLM 5.2 or Qwen3.8-Max?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.2 suits open-weight deployments; Qwen3.8-Max suits agentic coding.
Can I self-host GLM 5.2 or Qwen3.8-Max?
GLM 5.2 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Qwen3.8-Max 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.