GLM-5.3-Flash vs GPT-6.1 Sol
Which one, when
glm-5.3-flash is the cheaper of the two by a wide margin, at $0.15 input and $0.5 output per million versus $2 and $10 for gpt-6.1-sol, roughly 13x less on input and 20x less on output, with cache reads at $0.03 against $0.1. It also accepts video alongside text and images and allows up to 163840 output tokens, so pick it for high-volume, long-context or video-input work; context windows are effectively comparable at 1000000 versus 1050000. Choose gpt-6.1-sol when you specifically want OpenAI's newer generation, released 2026-09-29 with an April 2026 knowledge cutoff, and its 128000-token output ceiling is enough.
Benchmarks
GPT-6.1 Sol: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
Pricing
| GLM-5.3-Flash | GPT-6.1 Sol | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.15 | $2 | 0.075× |
| Output / 1M tokens | $0.5 | $10 | 0.05× |
| Cache read / 1M tokens | $0.03 | $0.1 | 0.3× |
| 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 76 chat models on this billing unit (log scale)
Capabilities
| GLM-5.3-Flash | GPT-6.1 Sol | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | always on | always on |
| 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-5.3-Flash | GPT-6.1 Sol | |
|---|---|---|
| Input modalities | text image video | text image |
| Output modalities | text | text |
| Released | - | 2026-09-29 |
| Knowledge cutoff | - | 2026-04 |
| Context window | 1M | 1.1M |
| Max output | 164K | 128K |
| Thinking parameter | reasoning_effort | reasoning.effort |
| Accepted values | reasoning_effort
| reasoning.effort
|
| Default | max | 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-5.3-Flash · GPT-6.1 Sol
One prompt, both models - measured through the gateway
GLM-5.3-Flash passed · 3 sentences
Enabling reasoning means the model now generates thousands of invisible "thinking" tokens before each visible answer, and we're billed for those hidden tokens at full output rates—our most expensive tier. Since output tokens cost 3–5x more than input tokens, that hidden thinking volume compounds across every request, which is why the bill doubled rather than edged up. If the spend concerns you, the fix is straightforward: route only complex queries to reasoning models and keep simple tasks on standard ones, which can reclaim a meaningful share of that cost without losing accuracy where it matters.
out 742 tok (+626 thinking) latency 23.2 s
GPT-6.1 Sol passed · 3 sentences
Enabling reasoning makes the model do more computational work before answering, and many providers bill the resulting reasoning tokens even though users never see them. That means the same number of requests can cost twice as much because each request consumes more billable tokens, even if the visible answers stay the same length. We should confirm the increase in our usage breakdown and reserve higher reasoning levels for tasks where the improvement justifies the added cost.
out 144 tok (+54 thinking) latency 6.6 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GLM-5.3-Flash 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 best = max(best, cur) else: cur = 1 return best ``` **The bug:** `cur` is supposed to hold the length of the current run, but it's seeded with `0` and reset to `0` on a mismatch instead of `1` — and since the loop starts at index 1, the first element of every run never passes through the `==` branch and so never gets counted at all. That means `cur` actually holds *run length minus 1* for every run, so every run is underreported by one: a single-element input never enters the loop and returns 0 instead of 1, and a run of length n returns n − 1 (e.g., `[3, 3, 3]` returns 2). […]
out 2462 tok (+2138 thinking) latency 29.1 s
GPT-6.1 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 counts equal adjacent pairs rather than items in a run: a run of length `n` has only `n - 1` equal pairs, so a single-element input returns `0`, and a run ending at the last index is also undercounted. It does process the last index, so the issue is not a missing final check. Initializing each nonempty run to `1` and updating `best` after every iteration correctly counts items, including singleton and trailing runs; empty inputs still return `0`.
out 189 tok latency 6.3 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GLM-5.3-Flash 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" } ``` Notes on judgment calls: revenue of 412 is on a constant-currency basis as stated; growth of "eleven and change" is approximated as 11 since no precise figure is given.
out 717 tok (+616 thinking) latency 9.1 s
GPT-6.1 Sol passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Withheld pending the Q4 close" }
out 345 tok (+285 thinking) latency 10.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GLM-5.3-Flash passed · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, the newest feature in our API gateway. Today, teams send identical prompts to multiple LLM providers and pay full price each time. Why duplicate that work and cost? With Cross-Provider Prompt Cache, your gateway stores prompt-response pairs and serves repeated requests from cache, regardless of which provider handles the call. The result: lower latency, reduced spend, and consistent outputs across OpenAI, Anthropic, Google, and self-hosted models. Configure cache policies per route, set TTLs, and invalidate entries instantly through the dashboard or API. Built-in analytics show hit rates and savings in real time. Enable the cache with a single flag, no code changes required. Available today on all paid plans. Contact sales for enterprise volume pricing details.
out 2095 tok (+1937 thinking) latency 20.2 s
GPT-6.1 Sol passed · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, a new API gateway feature that stores reusable prompts and manages caching across your supported AI providers. Why rebuild the same context every time your application switches models? With one configuration, teams can reuse shared instructions, standardize cache policies, and reduce repeated prompt processing wherever provider caching is available. The gateway handles provider-specific requirements while giving you clear visibility into cache hits, usage, and estimated savings. Set expiration windows, isolate cached content by project, and invalidate entries when prompts change. Your existing routing logic stays intact, so you can compare models without rebuilding your caching workflow. […]
out 588 tok (+435 thinking) latency 13.9 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="glm-5.3-flash",
# model="gpt-6.1-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-5.3-flash",
// model: "gpt-6.1-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-5.3-flash",
# "model": "gpt-6.1-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-5.3-flash",
// Model: "gpt-6.1-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-5.3-flash")
// .model("gpt-6.1-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-5.3-Flash or GPT-6.1 Sol?
GLM-5.3-Flash is cheaper on input / 1m tokens ($0.15 vs $2, 13× 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-5.3-Flash against GPT-6.1 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-5.3-Flash and GPT-6.1 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.