GPT-5.4 is OpenAI's frontier model for coding and professional work, positioned on its model page as "a more affordable model for coding and professional work."
- Input
- text image $2.5/M
- Output
- text $15/M
- Cache read
- $1.25/M
- Context
- 922K
- vs GPT-4o
- ~50% cheaper
- Knowledge cutoff
- 2025-08
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Price in context
Where the price sits among 60 comparable models
The bar shows how this model’s price compares with every other model of the same kind on Synthorai. The cheapest and the most expensive are named at each end. These are base rates; batch, region and cache-write discounts are on the pricing page.
Specs & limits
Tokens
| Context window (vendor spec) | 1,050,000 |
|---|---|
| Max output (vendor spec) | 128,000 |
| Knowledge cutoff | 2025-08 |
Prompt caching
| How it caches | automatic |
|---|---|
| Min prefix | 1,024 |
| Lifetime | 5-10m, up to 1h |
Thinking
| Vendor control | reasoning.effort |
|---|---|
| Accepted values | none · low · medium · high · xhigh |
| Default | none applied when the request sets nothing |
| Can be turned off | Yes |
| Thinking behaviour | Supports interleaved thinking: visible output before and between thinking, and thinking between tool calls. |
| Parameter | reasoning_effort |
| Values | minimal · low · medium · high the gateway's parameter surface - the vendor mapping above applies |
Model
| Modalities | text + image → text |
|---|
- 1.05M-token context / 128k max output
- prompts over 272k input tokens billed at 2x input / 1.5x output
- reasoning effort none-xhigh
One prompt, measured through the gateway
GPT-5.4 passed · 3 sentences
Enabling reasoning makes the model generate and evaluate additional internal steps before producing an answer, which increases the total tokens processed and therefore the cost per request. It also tends to increase latency and can trigger more tool calls, retries, or longer context retention, all of which compound usage-based charges. So even if user traffic stayed flat, the average cost per interaction rose materially once reasoning was turned on.
out 82 tok latency 1.9 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GPT-5.4 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 else: best = max(best, cur) cur = 1 best = max(best, cur) return best ``` The bug is that the original code was counting *matching transitions* between adjacent elements instead of the *length of the run itself*, so a single element started at `0` instead of `1`, making all run lengths off by one. It also only updated `best` inside the equality case, which means the final run could be lost if the loop ended before a change triggered any correction logic. […]
out 243 tok latency 3.0 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GPT-5.4 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 39 tok latency 1.3 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GPT-5.4 passed · 126 words, 0 banned, 1 question
Today we’re introducing Prompt Cache, a new API gateway feature that stores prompt results and reuses them across supported model providers. Teams can reduce latency, lower repeated compute costs, and keep application behavior more consistent when identical or near-identical prompts appear in production. Prompt Cache works at the gateway layer, so developers can adopt it without rewriting provider integrations or changing routing logic. Built-in controls let you set cache keys, expiration policies, and privacy rules for sensitive workloads. Need faster responses without duplicating optimization work for each vendor? With shared observability, you can track hit rates, savings, and cache performance in one place. […]
out 154 tok latency 2.9 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use GPT-5.4 in 30 seconds
OpenAI-compatible: swap the base_url, keep your SDK. POST /v1/chat/completions
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="gpt-5.4",
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: "gpt-5.4",
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": "gpt-5.4",
"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: "gpt-5.4",
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("gpt-5.4")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About GPT-5.4
- It expands the family to a 1,050,000-token context window (vendor spec) with 128K max output tokens, supports image input, configurable reasoning effort from none to xhigh, discounted cached input, and the full hosted tool set including web search and code interpreter, with an August 2025 knowledge cutoff.
- Reasoning effort defaults to none, which keeps latency low but means deliberate work has to be requested.
- At launch OpenAI called out four capabilities as new for this model: the 1M-class context window, tool search, built-in computer use, and compaction for long-running sessions.
- Beyond those, the tool list runs to file search, image generation, code interpreter, hosted shell, apply patch, skills, and MCP, and Chat Completions, Responses, and Batch are all supported.
- One pricing mechanic is worth planning around: prompts above 272K input tokens are billed at 2x input and 1.5x output for the entire request, so the surcharge is triggered by input size but raises the output rate too.
- It is the default choice when flagship capability must meet production economics.
- Synthorai customers reach GPT-5.4 through the gateway's OpenAI-compatible chat completions endpoint.
FAQ
Is the GPT-5.4 API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $2.5/M input tokens, that credit alone covers roughly 49 requests of ~8K tokens against GPT-5.4.
What is GPT-5.4 best at?
1,050,000-token context window; more affordable flagship-class option; full hosted tool set including web search. See the About section for the full picture from the vendor's own release notes.
How much does GPT-5.4 cost?
GPT-5.4 costs $2.5 per million input tokens and $15 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $1.25/M.
Does GPT-5.4 support prompt caching?
Yes, automatically: OpenAI-served prompts cache with no code changes. Cached input tokens bill at $1.25/M vs $2.5/M uncached; prompts need a 1,024-token stable prefix to cache (TTL 5-10m, up to 1h). Prompt caching guide →
How do I get access to GPT-5.4?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gpt-5.4", and you're done. One API key covers every model on the gateway.
What is GPT-5.4's knowledge cutoff?
GPT-5.4's knowledge cutoff is 2025-08, per the vendor's official documentation (as of 2026-07-09).
Related models
Compare
Every value on this page is transcribed from the vendor's own documentation, linked above, and carries the date it was checked. Prices are compared across the catalogue; specification values that vendors define differently are shown with the difference stated rather than charted. Nothing here is measured by us, and nothing is scored.