GLM-5.2 vs GPT-5.6 Sol
GLM-5.2 wins on Overall; GPT-5.6 Sol wins on 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 GPT-5.6 Sol if you…
- Enhanced reasoning
- Strong code generation
- o-series architecture
- Best for: Reasoning-heavy tasks, Coding, Math
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 | GPT-5.6 Sol | Verdict |
|---|---|---|---|
| Vendor | Zhipu AI (CN) | OpenAI (US) | Different vendors |
| Released | 2026.06 | 2026.07 | GPT-5.6 Sol is newer |
| Overall (rank) | 73 · #10 | 72 · #11 | GLM-5.2 +1 |
| Coding | 66 · #15 | 88 · #3 | GPT-5.6 Sol +22 |
| Multimodal | 79 · #12 | 80 · #11 | GPT-5.6 Sol +1 |
| Context window | 128K | 1.05M | GPT-5.6 Sol larger |
| Max output | 64K | 128K | GLM-5.2 longer |
| Effective-context | 88 | 88 | Tie |
| Input $/1M | $1.4 | $5 | GLM-5.2 cheaper |
| Output $/1M | $4.4 | $30 | GLM-5.2 cheaper |
| Cache discount | none | 50% off | GPT-5.6 Sol deeper |
| Speed | ~50 tok/s | ~25 tok/s | GLM-5.2 faster |
| TTFT | 0.6s | 2.0s | GLM-5.2 snappier |
| Function calling | 80 | 93 | GPT-5.6 Sol ahead |
| Refusal rate | ~8% | ~14% | GLM-5.2 less restrictive |
| English | 70 | 92 | GPT-5.6 Sol |
| Chinese | 90 | 74 | GLM-5.2 |
| Modalities | text, image | text, image | Same |
| Open weights | Yes | No | GLM-5.2 is open |
| Fine-tuning | Yes | No | |
| Free tier | ChatGLM free; free quota on the open platform | No free API tier | |
| SOC2 / no-train | no / yes | yes / yes | |
| Private deployment | Yes | No | GLM-5.2 |
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 1 points (73 vs 72). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GPT-5.6 Sol remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a decisive gap: GPT-5.6 Sol scores 88 against 66. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-5.6 Sol needs fewer correction turns; GLM-5.2 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GPT-5.6 Sol leads multimodal 80 vs 79. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
GLM-5.2 is faster in interactive use: ~50 tok/s with 0.6s TTFT versus ~25 tok/s with 2.0s TTFT (about 2.0× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 128K for GLM-5.2 and 1.05M for GPT-5.6 Sol. Crucially, the larger nominal window does not win on usable recall: GPT-5.6 Sol advertises 1.05M but GLM-5.2 scores higher on effective-context (88 vs 88), i.e. it actually retains more of what it was given.
Price & total cost
GLM-5.2 is the cheaper API at $1.4/$4.4 versus GPT-5.6 Sol at $5/$30 per 1M input/output tokens — list input is about 3.6× lower. Cache discounts (GPT-5.6 Sol 50%) shift the effective bill, worked out below.
Chinese vs English
English: GLM-5.2 70 vs GPT-5.6 Sol 92. Chinese: 90 vs 74. 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 GPT-5.6 Sol 93, so GPT-5.6 Sol has the edge on structured tool use. Fine-tuning is available from GLM-5.2. 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.
| Scenario · per month | GLM-5.2 | GPT-5.6 Sol | Gap |
|---|---|---|---|
| List priceno cache applied | $272100M in × $1.4 + 30M out × $4.4 | $1,400100M in × $5 + 30M out × $30 | 5.15×gap |
| With caching90% of inputs cache-hit | $272no published cache discount | $1,17590M cached in × $2.5 + 10M in × $5 + 30M out × $30 | 4.32×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.