Claude Opus 5 vs GLM-5.1
Which one, when — curated verdict, not a benchmark table
Pick claude-opus-5 when you need image input alongside text, a 1000000-token context window, or its explicit thinking capability; it runs about 3.6x the input rate and roughly 5.7x the output rate of glm-5.1 ($5/$25 versus $1.4/$4.4 per million). Choose glm-5.1 for text-only work that fits in 200000 tokens, where the lower rate card and $0.26 cache reads matter more than image support or the larger window. Both expose chat, code, tools and reasoning, allow thinking to be turned off, and cap output at a similar size (128000 versus 131072).
Pricing
| Claude Opus 5 | GLM-5.1 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $5 | $1.4 | 3.6× |
| Output / 1M tokens | $25 | $4.4 | 5.7× |
| Cache read / 1M tokens | $0.5 | $0.26 | 1.9× |
| Cache write | 1.25x (5m) / 2x (1h) | — | — |
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
| Claude Opus 5 | GLM-5.1 | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | yes | yes |
| Prompt caching | explicit (you mark the prefix) | implicit (automatic) |
| Cache lifetime | 5m default, 1h option | not published |
| Minimum cached prefix | 1024 tokens | not published |
Specs
| Claude Opus 5 | GLM-5.1 | |
|---|---|---|
| Input modalities | text image | text |
| Output modalities | text | text |
| Released | 2026-07-24 | 2026-04-07 |
| Knowledge cutoff | 2026-05 | — |
| Context window | 1M | 200K |
| Max output | 128K | 131K |
| Thinking parameter |
| thinking.type |
| Accepted values | thinking.type
effort
| thinking.type
|
| Default | thinking on effort
| enabled, and the model automatically determines whether to think |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Claude Opus 5 · GLM-5.1
One prompt, both models — measured through the gateway
Claude Opus 5 passed · 3 sentences
When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.
out 529 tok latency 9.0 s
GLM-5.1 passed · 3 sentences
Enabling reasoning requires the LLM to generate thousands of invisible "thinking" tokens to work through complex logic before producing the final answer. Because we are billed on total token volume, these intermediate steps are charged at the premium output rate, drastically increasing the variable cost per query. The invoice doubled simply because the model is now doing roughly twice the computational work per request, trading higher variable compute costs for significantly improved analytical accuracy.
out 913 tok (+827 thinking) latency 10.9 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Claude Opus 5 passed · 8/8 cases
## Fixed version ```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 else: cur = 1 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]
out 1094 tok latency 16.1 s
GLM-5.1 passed · 8/8 cases
```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 best = max(best, cur) else: cur = 1 return best ``` The original function tracks the number of adjacent equal pairs rather than the actual run length. By initializing `cur` and `best` to 0 and incrementing `cur` only when a match occurs, a run of length L produces a count of L−1. This causes single‑element inputs to return 0 (since the loop never runs) and generally returns a value one less than the true run length, effectively omitting the final element of any run—including those that end at the last index. […]
out 2642 tok (+2414 thinking) latency 26.1 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Claude Opus 5 passed · 5/5 fields, guidance "Withheld pending Q4 close"
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.
out 539 tok latency 7.6 s
GLM-5.1 passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null } ```
out 1465 tok (+1410 thinking) latency 16.1 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Claude Opus 5 passed · 123 words, 0 banned, 1 question
**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.
out 1593 tok latency 19.1 s
GLM-5.1 passed · 120 words, 0 banned, 1 question
We are thrilled to introduce prompt caching across providers in the API gateway. Why pay twice for the same prompt context? Now, when your application sends identical prompt prefixes to different LLM providers, our gateway automatically caches the input, reducing latency and cutting costs. This feature intelligently recognizes repeated prompt structures across OpenAI, Anthropic, and others, storing them efficiently at the network edge. Developers no longer need to manage separate caching logic for each individual provider. Instead, our unified system handles it directly, ensuring faster response times on all subsequent requests. Stop wasting valuable tokens on redundant processing workloads. Upgrade your integration today and experience immediate performance gains while keeping your infrastructure simple and your overall monthly billing incredibly low.
out 7589 tok (+7447 thinking) latency 188.9 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="claude-opus-5",
# model="glm-5.1", # 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: "claude-opus-5",
// model: "glm-5.1", // 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": "claude-opus-5",
# "model": "glm-5.1", # 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: "claude-opus-5",
// Model: "glm-5.1", // 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("claude-opus-5")
// .model("glm-5.1") // 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, Claude Opus 5 or GLM-5.1?
GLM-5.1 is cheaper on input / 1m tokens ($1.4 vs $5, 3.6× 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 Claude Opus 5 against GLM-5.1 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 Claude Opus 5 and GLM-5.1 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.