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Claude Fable 5.1 vs Gemini 3.6 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-3.6-flash costs about 13x less on both input ($0.75 vs $10 per million) and output ($3.75 vs $50) than claude-fable-5-1, and it also accepts video and audio, where claude-fable-5-1 takes text and image. claude-fable-5-1 allows 128000 output tokens against 65536 and lists thinking and reasoning capabilities, with a 2026-06 knowledge cutoff, while the context windows are close (1000000 vs 1048576). Pick gemini-3.6-flash for high-volume or multimodal work, and claude-fable-5-1 for long reasoning-heavy generations.

Benchmarks

Above averageNo peer higherClaude Fable 5.119 / 213 / 21Gemini 3.6 Flash6 / 181 / 18
Claude Fable 5.1 Gemini 3.6 Flash other models measured peer average ★ no peer scored higher
DeepSWE 1.1
67.4%
48.6%
BioMysteryBench hard
N/A
41.2%
OSWorld 2.0
N/A
33.8%
HealthBench Professional
58.1%
N/A
Terminal-Bench-Science 0.1
52.6%
N/A
Harvey Lab-AA
N/A
85.1%
GPQA Diamond
93.7%
N/A
AutomationBench
31.4%
17%
LVBench
N/A
84.2%

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

Pricing

Claude Fable 5.1 Gemini 3.6 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 3.6 Flash
Tool calling yes yes
Thinking control always on always on
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 3.6 Flash
Input modalities text image text image audio video
Output modalities text text
Released 2026-09-01 2026-07-21
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) thinkingLevel
Accepted values
effort
  • low
  • medium
  • high
  • xhigh
  • max
  • minimal
  • low
  • medium
  • high
Default

thinking always on (adaptive)

effort
  • high
medium

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 3.6 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 3.6 Flash passed · 3 sentences

Enabling reasoning causes the model to generate thousands of internal "thinking" tokens behind the scenes to process logic before delivering the final response. Because our vendor bills us for every token generated—including these intermediate processing steps—our billable output volume per query surged significantly. Since output tokens carry a much higher unit cost than input tokens, this jump in processing volume directly doubled our overall spend.

out 893 tok (+816 thinking) latency 4.7 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 3.6 Flash passed · 8/8 cases

Here is the fixed function along with an explanation of the bugs: ```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 The original function initialized `cur` and `best` to `0` and incremented `cur` on matching adjacent items, which meant it was counting identical adjacent *pairs* rather than the total number of *elements* in a run. This caused single-element inputs to skip the loop and return `0` instead of `1`, and undercounted all run lengths by 1 (for example, a run of two items at the end of the list resulted in `cur = 1`). […]

out 2034 tok (+1768 thinking) latency 10.0 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 3.6 Flash passed · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```

out 2843 tok (+2783 thinking) latency 13.1 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 3.6 Flash missed · 60 words, 0 banned, 0 questions

72: reducing 73: your 74: monthly 75: token 76: spend. S6 (21): 77: You 78: can 79: easily 80: set 81: custom 82: expiration 83: rules, 84: configure 85: TTL 86: settings, 87: and 88: manage 89: cache 90: invalidation 91: across 92: all 93: vendors 94: from 95: one 96: centralized 97: dashboard. S7 (23): 98: Start 99: optimizing […]

out 4092 tok (+3929 thinking) latency 16.3 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-3.6-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 3.6 Flash?

Gemini 3.6 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 3.6 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 3.6 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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