Claude Sonnet 5.5 vs Gemini 3.7 Flash
Which one, when
Both models bill per million tokens and land within a hair of each other on context (1000000 for claude-sonnet-5-5 versus 1048576 for gemini-3.7-flash), so the real split is price and inputs: Anthropic's rates of $2 input, $10 output and $0.2 cache read run about 2.7x Google's $0.75, $3.75 and $0.075 across the board. Pick claude-sonnet-5-5 when you need up to 128000 output tokens in one response, twice gemini-3.7-flash's 65536 ceiling. Pick gemini-3.7-flash for the cheaper rate card and for audio or video inputs, which the text-and-image-only Anthropic model cannot accept.
Benchmarks
Claude Sonnet 5.5: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
Pricing
| Claude Sonnet 5.5 | Gemini 3.7 Flash | Δ | |
|---|---|---|---|
| Input / 1M tokens | $2 | $0.75 | 2.7× |
| Output / 1M tokens | $10 | $3.75 | 2.7× |
| Cache read / 1M tokens | $0.2 | $0.075 | 2.7× |
| Cache write | 1.25x (5m) / 2x (1h) | - | - |
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
| Claude Sonnet 5.5 | Gemini 3.7 Flash | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | always on | yes - vendor dial not published |
| Structured output | yes | yes |
| Prompt caching | explicit (you mark the prefix) | implicit + explicit |
| Cache lifetime | 5m default, 1h option | not published |
| Minimum cached prefix | 1024 tokens | 4096 tokens |
Specs
| Claude Sonnet 5.5 | Gemini 3.7 Flash | |
|---|---|---|
| Input modalities | text image | text image audio video |
| Output modalities | text | text |
| Released | 2026-09-28 | 2026-08-13 |
| Knowledge cutoff | 2026-06 | 2026-03 |
| Context window | 1M | 1M |
| Max output | 128K | 66K |
| Thinking parameter | thinking.type | - |
| Accepted values | thinking.type
| - |
| Default | adaptive, effort high | - |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Claude Sonnet 5.5 · Gemini 3.7 Flash
One prompt, both models - 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
Gemini 3.7 Flash passed · 3 sentences
Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.
out 878 tok (+799 thinking) latency 6.9 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Claude Sonnet 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
Gemini 3.7 Flash passed · 8/8 cases
Here is the corrected function: ```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: cur = 1 best = max(best, cur) return best ``` ### Explanation of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.
out 1050 tok (+827 thinking) latency 6.4 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
Gemini 3.7 Flash passed · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```
out 918 tok (+858 thinking) latency 6.2 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
Gemini 3.7 Flash passed · 120 words, 0 banned, 1 question
Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.
out 2858 tok (+2718 thinking) latency 14.1 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="claude-sonnet-5-5",
# model="gemini-3.7-flash", # 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: "claude-sonnet-5-5",
// model: "gemini-3.7-flash", // 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": "claude-sonnet-5-5",
# "model": "gemini-3.7-flash", # 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: "claude-sonnet-5-5",
// Model: "gemini-3.7-flash", // 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("claude-sonnet-5-5")
// .model("gemini-3.7-flash") // 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, Claude Sonnet 5.5 or Gemini 3.7 Flash?
Gemini 3.7 Flash is cheaper on input / 1m tokens ($0.75 vs $2, 2.7× 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 Claude Sonnet 5.5 against Gemini 3.7 Flash 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 Claude Sonnet 5.5 and Gemini 3.7 Flash 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.