Claude Sonnet 4.6 is the generation that brought big-context capability to the Sonnet tier: versus Sonnet 4.5 it expands the context window from 200K to 1M tokens, doubles max output to 128K tokens, and adds adaptive thinking alongside extended thinking.
- Input
- text image $3/M
- Output
- text $15/M
- Cache read
- $0.3/M
- Context
- 1M
- vs GPT-4o
- ~40% cheaper
- Knowledge cutoff
- 2025-08
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance Google Moonshot OpenAI
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) | 1,000,000 |
|---|---|
| Max output (vendor spec) | 128,000 |
| Knowledge cutoff | 2025-08 |
| Training data through | 2026-01 knowledge cutoff 2025-08 |
Prompt caching
| How it caches | explicit (opt-in) |
|---|---|
| Min prefix | 1,024 |
| Lifetime | 5m default, 1h option |
| Write cost | 1.25x (5m) / 2x (1h) |
Thinking
| Vendor control | thinking.type + output_config.effort (budget_tokens still accepted, deprecated) |
|---|---|
| Accepted values | thinking.type adaptive · enabled (deprecated) · disabled; effort low · medium · high · max (no xhigh) |
| Default | thinking off; effort high applied when the request sets nothing |
| Can be turned off | Yes |
| Thinking behaviour | Adaptive thinking interleaves between tool calls automatically with no beta header; in manual "enabled" mode the interleaved-thinking-2025-05-14 header still works but is deprecated; earlier turns' thinking blocks are kept in context and billed as input. |
| Parameter | reasoning_effort |
| Values | minimal · low · medium · high the gateway's parameter surface - the vendor mapping above applies |
Model
| Modalities | text + image → text |
|---|
- 1M-token context GA at standard pricing since 2026-03-13
- up to 300k output tokens on Batch API with output-300k-2026-03-24 beta header
One prompt, measured through the gateway
Claude Sonnet 4.6 passed · 3 sentences
Here is a three-sentence explanation for a CFO: Reasoning models "think out loud" by generating a long internal chain of thought before producing the final answer, and every token in that thinking process is billed at the same rate as the output you actually see. This means a single user request that previously cost 500 tokens of output may now cost 5,000 tokens once the hidden reasoning steps are counted, multiplying cost per call by roughly 5-10x depending on task complexity. To control this, we can cap reasoning depth, route simpler tasks back to non-reasoning models, and audit which use cases actually require that level of accuracy to ensure we are only paying the premium where it delivers measurable business value.
out 156 tok latency 4.9 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Claude Sonnet 4.6 passed · 8/8 cases
## The Bug There are two related off-by-one errors: 1. **`cur` starts at `0` instead of `1`** — a single matching pair increments `cur` to `1`, which correctly represents a run of 2, but a single-element list (or any non-repeating list) leaves `cur = 0` and `best = 0`, when the answer should be `1`. 2. **`best` is only updated inside the `if` branch** — a run that ends at the last index is captured correctly *in that branch*, but the initialization problem means the baseline "every element is a run of at least 1" is never established. The root cause is that **`cur` tracks the number of *consecutive equal pairs*, not the length of the run**. Switching to tracking run length directly (starting `cur = 1`, updating `best` on *every* iteration including the `else` branch reset) fixes both issues. […]
out 685 tok latency 12.2 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Claude Sonnet 4.6 passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```
out 67 tok latency 1.3 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Claude Sonnet 4.6 passed · 121 words, 0 banned, 1 question
**Introducing Prompt Caching Across Providers** Managing AI costs gets complicated when identical prompts hit multiple providers repeatedly, burning through tokens unnecessarily. Today, we're launching cross-provider prompt caching in our API gateway — a feature that stores prompt responses and serves cached results regardless of which underlying model handles your request. Here's how it works: when your application sends a repeated prompt, the gateway checks its cache first, returning stored outputs in milliseconds rather than forwarding requests to OpenAI, Anthropic, or any connected provider. Why pay for the same generation twice? With configurable TTL settings and cache-key customization, teams processing high-volume or repetitive workloads will see immediate latency improvements and measurable cost reductions starting on day one. Available now on all paid plans.
out 177 tok latency 5.4 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use Claude Sonnet 4.6 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-4-6",
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-4-6",
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-4-6",
"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-4-6",
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-4-6")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About Claude Sonnet 4.6
- Anthropic launched it as its balanced model combining speed and intelligence for everyday tasks, with improved agentic search performance while consuming fewer tokens, and the 1M window is generally available at standard pricing rather than beta-gated.
- It keeps Sonnet's fast latency profile and $3/$15-per-million-token pricing, with vision input and tool use throughout, and it qualifies for the Batch API's extended 300K-output beta.
- Effort covers low through max but not xhigh; although the parameter defaults to high, Anthropic explicitly recommends setting it on this model to avoid unexpected latency, and suggests medium as the practical default.
- Extended thinking still works but is deprecated in favour of adaptive, and neither thinking type is rejected outright.
- Prefilling the assistant message returns an error here, while non-default sampling parameters are still tolerated.
- That restriction arrives with Sonnet 5.
- It uses the older tokenizer, so token counts stay comparable with earlier models.
- Anthropic now lists it as a legacy model superseded by Claude Sonnet 5.
- Through Synthorai's OpenAI-compatible endpoint it drops into any existing GPT-style integration unchanged.
FAQ
Is the Claude Sonnet 4.6 API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $3/M input tokens, that credit alone covers roughly 41 requests of ~8K tokens against Claude Sonnet 4.6.
What is Claude Sonnet 4.6 best at?
Context expanded from 200K to 1M tokens; doubled max output to 128K tokens; adaptive thinking at unchanged $3/$15 pricing. See the About section for the full picture from the vendor's own release notes.
How much does Claude Sonnet 4.6 cost?
Claude Sonnet 4.6 costs $3 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 $0.3/M.
Does Claude Sonnet 4.6 support prompt caching?
Yes, via opt-in: mark stable prefixes with cache_control breakpoints. Cached input tokens bill at $0.3/M vs $3/M uncached; prompts need a 1,024-token stable prefix to cache (TTL 5m default, 1h option). Prompt caching guide →
How do I get access to Claude Sonnet 4.6?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="claude-sonnet-4-6", and you're done. One API key covers every model on the gateway.
What is Claude Sonnet 4.6's knowledge cutoff?
Claude Sonnet 4.6'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.