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Claude Fable 5.1 vs Gemini 3.7 Flash

Claude Fable 5.1 is served by invitation. Its figures below are the live rates, but calls need a workspace grant first; ask us for access before you build on this comparison.

vs

Which one, when

gemini-3.7-flash is the cheaper and broader-input option: $0.75 input and $3.75 output per million tokens, roughly 13x less than claude-fable-5-1 at $10 and $50, with a 1048576-token context and text, image, audio and video in (audio input is also priced per million tokens, at $0.75). Pick claude-fable-5-1 when you want its always-on thinking mode, which cannot be disabled, plus 128000 max output tokens versus 65536, over a 1000000-token context. If your inputs are text and images and you are cost-sensitive, the Google model covers it; cache reads are $0.075 against $0.25.

Benchmarks

Above averageNo peer higherClaude Fable 5.119 / 213 / 21Gemini 3.7 Flash17 / 233 / 23
Claude Fable 5.1 Gemini 3.7 Flash other models measured peer average ★ no peer scored higher
DeepSWE 1.1
67.4%
65.3%
BioMysteryBench hard
N/A
43.5%
OSWorld 2.0
N/A
47.9%
HealthBench Professional
58.1%
N/A
Finance Agent v2
N/A
59%
Harvey Lab-AA
N/A
90.7%
GPQA Diamond
93.7%
N/A
AutomationBench
31.4%
30.4%
LVBench
N/A
no peer scored higher 85.4%

Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

Pricing

Claude Fable 5.1 Gemini 3.7 Flash Δ
Input / 1M tokens $10 $0.75 13×
Output / 1M tokens $50 $3.75 13×
Cache read / 1M tokens $0.25 $0.075 3.3×
Cache write 1.25x (5m) / 2x (1h) - -

Rates from the live catalogue at build time; each model page carries the current rate card.

Where they sit · input price per 1M tokens across all 79 chat models on this billing unit (log scale)

Capabilities

Claude Fable 5.1 Gemini 3.7 Flash
Tool calling 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 Fable 5.1 Gemini 3.7 Flash
Input modalities text image text image audio video
Output modalities text text
Released 2026-09-01 2026-08-13
Knowledge cutoff 2026-06 2026-03
Context window 1M 1M
Max output 128K 66K
Thinking parameter output_config.effort (thinking is adaptive-only and always on) -
Accepted values
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
Default

thinking always on (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 Fable 5.1 · Gemini 3.7 Flash

One prompt, both models, measured through the gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. CHECK exactly 3 sentences

Claude Fable 5.1 passed · 3 sentences

When we enabled reasoning, the model started generating a long chain of internal "thinking" tokens before producing each answer, and we're billed for those tokens at the same output rate even though users never see them. In practice, that hidden reasoning often runs several times longer than the visible response, so the total tokens per request roughly doubled while the number of requests stayed flat. The upside is measurably better accuracy on complex tasks, so the right conversation is whether that quality gain justifies the cost—and if not, we can restrict reasoning to the workflows that actually need it rather than turning it on globally.

out 186 tok latency 3.2 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, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. CHECK fix passes tests

Claude Fable 5.1 passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): cur = cur + 1 if xs[i] == xs[i - 1] else 1 best = max(best, cur) return best ``` The bug is that `cur` counts *matches between adjacent elements* rather than *elements in the run*: it starts at 0 and only increments when `xs[i] == xs[i-1]`, so a run of length k produces a count of k−1. That's why a single element returns 0 instead of 1, and why every other run also comes out one short — the "misses runs at the end" reports are the same off-by-one seen on inputs like `[1, 2, 2]`, where the trailing run of length 2 is reported as 1 (the original code does update `best` inside the loop, so end-of-list runs aren't actually skipped, just undercounted). […]

out 882 tok (+507 thinking) latency 3.4 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.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. CHECK valid JSON, schema exact

Claude Fable 5.1 passed · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on judgment calls (not part of the object): - **revenue_usd_m**: The text says "412 for the topline" but never states the currency or that the figure is in millions; it's also on a constant-currency basis and prefaced with "call it," so it's approximate. If you want strict literalism, this should be `null`. - **growth_pct**: "Eleven and change" means slightly above 11%; the exact figure isn't given. - **quarter**: "September quarter" is Q3 on a calendar basis, consistent with the reference to a pending Q4 close. - **guidance**: Explicitly withheld, so `null`.

out 633 tok (+349 thinking) latency 3.8 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.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. CHECK 120 words, 0 banned words

Claude Fable 5.1 passed · 121 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching for the Gateway.** Starting today, your API gateway can cache prompts once and reuse them across OpenAI, Anthropic, Google, and any other provider you route to. Long system instructions, retrieval context, and few-shot examples are stored at the gateway layer, so switching models no longer means paying to resend the same tokens. Why send the same 8,000-token preamble to three different vendors when you could send it once? Our cache normalizes prompt segments, fingerprints them, and serves matching prefixes from memory before the request ever reaches an upstream API. Early customers report token costs falling by 30 to 60 percent on high-volume workloads, with median latency dropping alongside. Available today on all Team and Enterprise plans.

out 1722 tok (+1463 thinking) latency 2.9 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-fable-5-1",
    # 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)

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FAQ

Which is cheaper, Claude Fable 5.1 or Gemini 3.7 Flash?

Gemini 3.7 Flash is cheaper on the "Input / 1M tokens" row ($0.75 vs $10, 13× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.

Can I A/B test Claude Fable 5.1 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 change to the model id, so you can route a fraction of traffic to each and compare bills directly.

Do Claude Fable 5.1 and Gemini 3.7 Flash support prompt caching?

Yes. Both bill cache 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.

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