GLM-5.2 vs Muse Spark 1.1
GLM-5.2 wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose GLM-5.2 if you…
- Strong open-source ecosystem
- Good Chinese
- Mature enterprise services
- Best for: Chinese, Open source, Coding
Choose Muse Spark 1.1 if you…
- Completely free open weights
- By Meta
- Self-hostable
- Multimodal
- Best for: Open research, Local deployment, Experimental
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | GLM-5.2 | Muse Spark 1.1 | Verdict |
|---|---|---|---|
| Vendor | Zhipu AI (CN) | Meta (US) | Different vendors |
| Released | 2026.06 | 2026.08 | Muse Spark 1.1 is newer |
| Overall (rank) | 73 · #10 | 61 · #22 | GLM-5.2 +12 |
| Coding | 66 · #15 | 59 · #21 | GLM-5.2 +7 |
| Multimodal | 79 · #12 | 75 · #15 | GLM-5.2 +4 |
| Context window | 128K | 256K | Muse Spark 1.1 larger |
| Max output | 64K | 64K | Tie |
| Effective-context | 88 | 80 | GLM-5.2 more reliable |
| Input $/1M | $1.4 | Free | Muse Spark 1.1 cheaper |
| Output $/1M | $4.4 | Free | Muse Spark 1.1 cheaper |
| Cache discount | none | none | Tie |
| Speed | ~50 tok/s | Hardware-dependent | GLM-5.2 faster |
| TTFT | 0.6s | Hardware-dependent | GLM-5.2 snappier |
| Function calling | 80 | 65 | GLM-5.2 ahead |
| Refusal rate | ~8% | ~4% | Muse Spark 1.1 less restrictive |
| English | 70 | 78 | Muse Spark 1.1 |
| Chinese | 90 | 60 | GLM-5.2 |
| Modalities | text, image | text, image | Same |
| Open weights | Yes | Yes | Both open |
| Fine-tuning | Yes | Yes | |
| Free tier | ChatGLM free; free quota on the open platform | Fully free (model weights) | |
| SOC2 / no-train | no / yes | no / yes | |
| Private deployment | Yes | Yes | Both support it |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
GLM-5.2 leads the overall aggregate by 12 points (73 vs 61). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Muse Spark 1.1 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a modest gap: GLM-5.2 scores 66 against 59. On multi-file edits, SWE-style tickets and long-horizon agent loops GLM-5.2 needs fewer correction turns; Muse Spark 1.1 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GLM-5.2 leads multimodal 79 vs 75. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
Muse Spark 1.1 is self-hosted, so its speed depends on your hardware; against a managed API like GLM-5.2 (~50 tok/s, 0.6s TTFT) compare on your own infrastructure before assuming latency.
Context: window vs usable recall
Nominal windows are 128K for GLM-5.2 and 256K for Muse Spark 1.1. Crucially, the larger nominal window does not win on usable recall: Muse Spark 1.1 advertises 256K but GLM-5.2 scores higher on effective-context (88 vs 80), i.e. it actually retains more of what it was given.
Price & total cost
Muse Spark 1.1 is free open weights (you pay only for the infrastructure you run it on), while GLM-5.2 is a paid API at $1.4/$4.4 per 1M tokens. The real comparison is total cost of ownership — GPU/ops against a managed bill — not list price alone.
Chinese vs English
English: GLM-5.2 70 vs Muse Spark 1.1 78. Chinese: 90 vs 60. For Chinese-language production, GLM-5.2 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: GLM-5.2 80 vs Muse Spark 1.1 65, so GLM-5.2 has the edge on structured tool use. Fine-tuning is available from GLM-5.2 and Muse Spark 1.1. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.