GLM-5.2 vs GPT-5.6 Sol
Which one, when — curated verdict, not a benchmark table
Both models are text-out reasoning models with roughly million-token context windows (1,000,000 for glm-5.2, 1,050,000 for gpt-5.6-sol) and optional thinking, so the real split is price and input modality. Pick glm-5.2 for text-only long-context work at volume: at $0.77 input and $2.42 output it runs about 6.5x cheaper on input and roughly 12x cheaper on output than gpt-5.6-sol's $5 and $30, with a slightly higher 131072 max output. Choose gpt-5.6-sol when you need image input, which glm-5.2 does not accept.
Pricing
| GLM-5.2 | GPT-5.6 Sol | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.77 | $5 | 0.15× |
| Output / 1M tokens | $2.42 | $30 | 0.081× |
| Cache read / 1M tokens | $0.143 | $0.5 | 0.29× |
| Cache write | — | no separate charge | — |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)
Capabilities
| GLM-5.2 | GPT-5.6 Sol | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | not published | 5–10m, up to 1h |
| Minimum cached prefix | not published | 1024 tokens |
Specs
| GLM-5.2 | GPT-5.6 Sol | |
|---|---|---|
| Input modalities | text | text image |
| Output modalities | text | text |
| Released | 2026-06-16 | 2026-07-09 |
| Knowledge cutoff | — | 2026-02 |
| Context window | 1M | 1.1M |
| Max output | 131K | 128K |
| Thinking parameter |
| reasoning.effort |
| Accepted values | thinking.type
reasoning_effort
| reasoning.effort
|
| Default | enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it | medium |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GLM-5.2 · GPT-5.6 Sol
One prompt, both models — measured through the gateway
GLM-5.2 passed · 3 sentences
Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.
out 1223 tok (+1138 thinking) latency 17.1 s
GPT-5.6 Sol passed · 3 sentences
Enabling reasoning makes the model generate additional internal tokens before producing an answer, so each request consumes more billable compute. Reasoning may also increase response length, latency, tool calls, and retries, further raising per-request cost. In short, usage volume may be unchanged, but the cost per task increased enough to double the bill.
out 111 tok (+34 thinking) latency 2.3 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GLM-5.2 missed · 1/8 cases (fails [1])
```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 […]
out 4097 tok (+4036 thinking) latency 58.4 s
GPT-5.6 Sol passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The original function counted equal *adjacencies* rather than items, so a run of length one produced `0` and every longer run was undercounted by one. Initializing `cur` and `best` to `1` for nonempty input makes them represent item counts, resetting `cur` to `1` starts a new run at the current item, and updating `best` on every iteration ensures a run reaching the final index is included.
out 316 tok (+135 thinking) latency 5.2 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GLM-5.2 passed · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```
out 1947 tok (+1893 thinking) latency 30.9 s
GPT-5.6 Sol passed · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":"11+","guidance":"withheld pending the Q4 close"}
out 194 tok (+147 thinking) latency 3.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GLM-5.2 passed · 120 words, 0 banned, 1 question
We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.
out 11125 tok (+10984 thinking) latency 114.8 s
GPT-5.6 Sol passed · 120 words, 0 banned, 1 question
Today, we’re introducing Provider Prompt Cache, a new API gateway feature that reuses prompt prefixes across supported AI providers, reducing latency, token costs, and duplicated processing. Teams can define cache policies once, route requests dynamically, and preserve provider flexibility without rewriting application logic. Switching models during testing or failover? The gateway identifies eligible prompt segments, applies provider-specific caching controls, and reports hits, misses, savings, and expiration details through unified logs and metrics. Configurable TTLs, tenant isolation, encryption, and cache-bypass options help teams balance performance, privacy, and freshness for every workload. Provider Prompt Cache is available today in beta through the dashboard and API, with SDK examples and migration guidance included. […]
out 733 tok (+564 thinking) latency 7.7 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="glm-5.2",
# model="gpt-5.6-sol", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "glm-5.2",
// model: "gpt-5.6-sol", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.2",
# "model": "gpt-5.6-sol", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "glm-5.2",
// Model: "gpt-5.6-sol", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("glm-5.2")
// .model("gpt-5.6-sol") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, GLM-5.2 or GPT-5.6 Sol?
GLM-5.2 is cheaper on input / 1m tokens ($0.77 vs $5, 6.5× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.
Can I A/B test GLM-5.2 against GPT-5.6 Sol without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do GLM-5.2 and GPT-5.6 Sol support prompt caching?
Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.