Claude Sonnet 5.5 is Anthropic's model for the best combination of speed and intelligence, released on September 28, 2026, and it is priced the same as Claude Sonnet 5: $2 per million input tokens and $10 per million output tokens, with prompt-cache reads at 10% of the input price ($0.20 per million), 5-minute cache writes at $2.50 and 1-hour cache writes at $4.
- Input
- text image $2/M
- Output
- text $10/M
- Cache read
- $0.2/M
- Context
- 1M
- vs GPT-4o
- ~60% cheaper
- Knowledge cutoff
- 2026-06
Price in context
Where the price sits among 68 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) | 1,000,000 |
|---|---|
| Max output (vendor spec) | 128,000 |
| Knowledge cutoff | 2026-06 |
Prompt caching
| How it caches | explicit (opt-in) |
|---|---|
| Min prefix | 512 provider default is 1,024 |
| Lifetime | 5m default, 1h option |
| Write cost | 1.25x (5m) / 2x (1h) |
Thinking
| Vendor control | thinking.type |
|---|---|
| Accepted values | adaptive (default) · between_tools |
| Default | adaptive, effort high applied when the request sets nothing |
| Can be turned off | No |
| Thinking behaviour | Adaptive thinking is on by default. The lowest setting, between_tools, turns off up-front thinking and works at high effort or below; thinking {"type": "disabled"} and a manual {"type": "enabled", "budget_tokens": N} both return a 400 error. |
| Parameter | reasoning_effort |
| Values | minimal · low · medium · high the gateway's parameter surface - the vendor mapping above applies |
Model
| Modalities | text + image → text |
|---|
- Same price as Claude Sonnet 5
- 1M context at standard pricing with no long-context tier
- prompt-cache reads cost 0.1x input ($0.20/M)
- minimum cacheable prompt 512 tokens
- setting temperature, top_p or top_k to a non-default value returns a 400
- forced tool use returns an error
- text between tool calls comes back in thinking blocks
- up to 300k output tokens on the Message Batches API with the output-300k-2026-03-24 beta header
One prompt, measured through the gateway
Claude Sonnet 5.5 passed · 3 sentences
Enabling reasoning makes the model "think" before it answers, and that internal thinking is generated as extra text (reasoning tokens) that we are billed for at the same rate as output tokens, even though users never see it. Output tokens also cost several times more than input tokens, so a single request that used to produce a short answer can now generate many times more billable text behind the scenes. Because our request volume and prompts didn't change, the doubling comes almost entirely from this hidden thinking, and we can bring it down by limiting reasoning effort, turning it off for simple tasks, and reserving it for the workloads where the accuracy gain justifies the cost.
out 205 tok latency 3.6 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Claude Sonnet 5.5 passed · 8/8 cases
**The bug:** `cur` counts the number of *adjacent equal pairs* rather than the number of items in the run. A run of length k contains only k−1 such pairs, so every result is one too small. The counter also starts at 0 and the loop starts at index 1, so a single-element list never updates `best` and returns 0 instead of 1. (An empty list should return 0.) In addition, `cur` resets to 0 on a mismatch, when it should reset to 1, because the item at `xs[i]` begins a new run of length 1. The claim that it "misses runs ending at the last index" is really the same off-by-one: the final run is counted, but as one less than its true length. Because `best` is updated inside the loop on every extension, the last run isn't skipped. […]
out 444 tok latency 6.7 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Claude Sonnet 5.5 passed · 5/5 fields, guidance null
Here is the extracted JSON: ```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on the choices: - **quarter**: The text says "September quarter" without specifying a fiscal year or Q-number, so I kept the wording as stated. - **revenue_usd_m**: The text gives "412" for the topline, with no currency or unit stated explicitly. I've taken it as 412 million USD per the field definition, but the text does not confirm that, and the figure is constant-currency. - **growth_pct**: "Eleven and change" means slightly above 11, so 11 is an approximation. The exact figure isn't given. - **guidance**: Guidance is withheld pending the Q4 close, so no guidance is stated, hence null.
out 300 tok latency 3.5 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Claude Sonnet 5.5 passed · 122 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching** Your API gateway now caches prompts across every connected LLM provider, whether you route traffic to OpenAI, Anthropic, Google, or a self-hosted model. Here's how it works: the gateway fingerprints each prompt prefix, stores it centrally, and applies the right provider-specific caching mechanism automatically. When you switch providers or fail over during an outage, your cached context follows you. Early testing shows input token costs dropping by up to 70% on repeated system prompts and long documents, with latency improvements of 40% or more on cache hits. Tired of paying to send the same instructions thousands of times a day? Enable it with one configuration flag. Cache rules, TTLs, and per-team isolation are available in the dashboard today.
out 266 tok latency 3.8 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use Claude Sonnet 5.5 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="claude-sonnet-5-5",
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-sonnet-5-5",
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-sonnet-5-5",
"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-sonnet-5-5",
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-sonnet-5-5")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About Claude Sonnet 5.5
- It keeps a 1M-token context window with no long-context surcharge and 128K max output, and accepts text and image input.
- Adaptive thinking is on by default with a default effort of high; the lowest setting, between_tools, turns off up-front thinking and works at high effort or below, while a disabled thinking block or a manual budget_tokens request returns a 400.
- Setting temperature, top_p or top_k to a non-default value also returns a 400, and forced tool use is not supported.
- Anthropic lists five breaking changes for code already running on Claude Sonnet 5, and text written between tool calls now comes back in thinking blocks, so applications that stream that text to users should check how they display it.
- The minimum cacheable prompt is 512 tokens.
- Synthorai serves Claude Sonnet 5.5 through the same OpenAI-compatible API as the rest of the fleet.
FAQ
Is the Claude Sonnet 5.5 API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $2/M input tokens, that credit alone covers roughly 62 requests of ~8K tokens against Claude Sonnet 5.5.
What is Claude Sonnet 5.5 best at?
The best combination of speed and intelligence in the lineup; Sonnet 5 pricing: $2 in, $10 out per million tokens; between_tools turns off up-front thinking. See the About section for the full picture from the vendor's own release notes.
How much does Claude Sonnet 5.5 cost?
Claude Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.2/M.
Does Claude Sonnet 5.5 support prompt caching?
Yes, via opt-in: mark stable prefixes with cache_control breakpoints. Cached input tokens bill at $0.2/M vs $2/M uncached; prompts need a 512-token stable prefix to cache (TTL 5m default, 1h option). Prompt caching guide →
How do I get access to Claude Sonnet 5.5?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="claude-sonnet-5-5", and you're done. One API key covers every model on the gateway.
What is Claude Sonnet 5.5's knowledge cutoff?
Claude Sonnet 5.5's knowledge cutoff is 2026-06, per the vendor's official documentation (as of 2026-09-29).
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.