GLM-5.1 vs Kimi K2.7 Code
Which one, when
Pick glm-5.1 when you need very long single responses or the option to turn thinking off: it allows up to 131072 output tokens against 32768 for kimi-k2.7-code, and its reasoning is optional rather than always on. Pick kimi-k2.7-code for image and video inputs, a 256000-token context instead of 200000, and a cheaper rate card at $0.95 input versus $1.4 (about 1.5x less) and $4 output versus $4.4. Both cover chat, code, reasoning and tools, so the real decision is output length and toggleable thinking against multimodal input and context.
Benchmarks
GLM-5.1: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Moonshot OpenAI Z.ai
Pricing
| GLM-5.1 | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.4 | $0.95 | 1.5× |
| Output / 1M tokens | $4.4 | $4 | 1.1× |
| Cache read / 1M tokens | $0.26 | $0.19 | 1.4× |
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
| GLM-5.1 | Kimi K2.7 Code | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | configurable | always on |
| Structured output | yes | - |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | not published | not published |
| Minimum cached prefix | not published | not published |
Specs
| GLM-5.1 | Kimi K2.7 Code | |
|---|---|---|
| Input modalities | text | text image video |
| Output modalities | text | text |
| Released | 2026-04-07 | 2026-06 |
| Context window | 200K | 256K |
| Max output | 131K | 33K |
| Thinking parameter | thinking.type |
|
| Accepted values | thinking.type
| type
keep
|
| Default | enabled, and the model automatically determines whether to think | thinking on with Preserved Thinking on |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GLM-5.1 · Kimi K2.7 Code
One prompt, both models, measured through the gateway
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
Kimi K2.7 Code passed · 3 sentences
Enabling reasoning mode causes the model to generate an internal chain-of-thought before producing each answer, and that hidden reasoning text is billed like any other output. Those extra reasoning tokens typically add up to much more than the final response itself, so every API call now costs significantly more than before. Because our overall usage volume did not change, the doubled bill reflects that each query consumes roughly twice as many billable tokens.
out 259 tok (+174 thinking) latency 5.4 s
Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
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
Kimi K2.7 Code 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 bug is that the function counts *transitions* between equal neighbors instead of counting the items themselves, so a single-element list never enters the loop and returns `0` instead of `1`. Likewise, resetting `cur` to `0` on a mismatch forgets the current item that starts a new run, which causes runs that end at the last index to be undercounted. Initializing both `best` and `cur` to `1` and resetting `cur` to `1` fixes both issues.
out 410 tok (+222 thinking) latency 9.4 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
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
Kimi K2.7 Code passed · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```
out 721 tok (+663 thinking) latency 13.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
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
Kimi K2.7 Code passed · 120 words, 0 banned, 1 question
We're introducing Prompt Cache for our API Gateway, a new capability that stores prompt responses across multiple AI providers. By caching replies, you reduce redundant calls, lower latency, and cut costs without changing how you build. Teams using several model providers can now reuse identical prompt results instantly, keeping traffic consistent and budgets predictable. Responses are keyed by provider, model, and exact prompt, so you always get the right result. Configure TTL, hit thresholds, and eviction rules from a single dashboard. It fits into your existing routing and requires no code changes. Setup takes minutes and works with your current endpoints. Want to see how much latency and spend you can trim? Check the docs to enable Prompt Cache today.
out 2375 tok (+2235 thinking) latency 38.6 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-5.1",
# model="kimi-k2.7-code", # 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-5.1",
// model: "kimi-k2.7-code", // 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-5.1",
# "model": "kimi-k2.7-code", # 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-5.1",
// Model: "kimi-k2.7-code", // 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-5.1")
// .model("kimi-k2.7-code") // 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-5.1 or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper on the "Input / 1M tokens" row ($0.95 vs $1.4, 1.5× 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 GLM-5.1 against Kimi K2.7 Code 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 GLM-5.1 and Kimi K2.7 Code 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.