Claude Opus 5 vs GLM-5.1
Which one, when
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).
Benchmarks
GLM-5.1: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
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 catalogue at build time; each model page carries the current rate card.
Where they sit · input price per 1M tokens across all 76 chat models on this billing unit (log scale)
Capabilities
| Claude Opus 5 | GLM-5.1 | |
|---|---|---|
| Tool calling | 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 shifts the LLM from generating quick answers to producing hidden "chain-of-thought" tokens that systematically explore multiple solution paths before responding. Because billing is based on total tokens processed, these invisible reasoning tokens drastically increase input and output volume per query, often adding thousands of compute-intensive tokens to a single prompt. Consequently, our bill doubled not from higher per-token rates or increased user volume, but from the exponentially deeper computational work required to deliver higher-quality, more reliable outputs.
out 98 tok latency 7.8 s
Instruction following (exactly three sentences, so 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 ``` **Explanation:** The original function initialized `cur` and `best` to `0` and only incremented `cur` when a duplicate was found. This counts the number of *extra* equal items in a run rather than the total run length, so every run is reported as one shorter than it actually is—returning `0` for single-element lists and `L-1` for a run of length `L` (including those ending at the last index). The fix initializes `cur` and `best` to `1` (when the list is non‑empty) and resets `cur` to `1` on a mismatch, so `cur` tracks the full length of the current run and `best` records the true maximum.
out 3823 tok (+3585 thinking) latency 49.7 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 "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```
out 1091 tok (+1028 thinking) latency 15.2 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 excited to introduce Prompt Cache for our API Gateway. This new feature stores responses for identical prompts, routing subsequent requests directly to the cache instead of calling the underlying AI provider. Are you tired of paying multiple times for the exact same query? Prompt Cache solves this by recognizing duplicate inputs across all supported providers, drastically reducing latency and operational costs. When a user submits a request that matches a previously cached prompt, the gateway returns the stored answer instantly. You can configure cache expiration and scope rules via your dashboard to maintain data freshness. Stop wasting budget on redundant computational work. Upgrade to the latest gateway tier today to start saving time and money on every call.
out 3935 tok (+3802 thinking) latency 41.1 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 the "Input / 1M tokens" row ($1.4 vs $5, 3.6× apart). Other rows may point the other way; the table above carries the full rate 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 change to the model id, 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 cache 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.