Z.ai
GLM 5.2
Z.ai's flagship GLM-5.2 coding model with a documented 1M-token context and 128K max output.
Catalog record checked August 14, 2026; individual provider fields may change.
| Specification | GLM 5.2 |
|---|---|
| Provider | Z.ai |
| Tier | Frontier |
| Context window | 1M |
| Max output | 128K |
| Input / 1M tokens | $1.40 |
| Output / 1M tokens | $4.40 |
| Weights | Open |
| Parameters | Not disclosed |
| Modalities | text |
| Released | June 16, 2026 |
Verified evidence
Published benchmark results
Each result keeps its source and measurement date visible. A missing benchmark is not treated as a zero.
Artificial Analysis Intelligence Index [max]
2026-08-08 · Artificial Analysis
Benchmark
DeepSWE 1.1 in context
The full local snapshot puts this model beside the wider field, including cost per completed task.
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.
Best for
- Open-weight deployments
- Long-horizon coding agents
- Long context on a budget
Watch out
Common questions
GLM 5.2
Answered from the verified figures on this page rather than general guidance.
How much does GLM 5.2 cost per million tokens?
GLM 5.2 is listed at $1.40 per million input tokens and $4.40 per million output tokens at standard rates. Output tokens usually dominate real bills, so weigh the output rate more heavily than the input rate.
What is GLM 5.2's context window?
GLM 5.2 accepts about 1M tokens of context. That only matters if you routinely send very long documents, large codebases, or multi-turn histories that approach that limit.
What is GLM 5.2 best for?
GLM 5.2 is a frontier tier from Z.ai. It suits open-weight deployments, long-horizon coding agents, long context on a budget. 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.
Can I self-host GLM 5.2?
GLM 5.2 publishes open weights, but self-hosting depends on the licence, hardware footprint, quantisation quality, and serving stack. A hosted API is often cheaper until you have measured throughput and concurrency on your own hardware.
Compare it
Head-to-head model comparisons
These are the published pairings that put this model against a plausible alternative.