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Claude Fable 5.1 vs Gemini 3.8 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

Released a day apart, both accept text and image input across roughly a million tokens of context, but the rate cards diverge sharply: claude-fable-5-1 runs about 13x more per token on both input ($10 vs $0.75) and output ($50 vs $3.75), and about 3x more on cache reads ($0.25 vs $0.075). Pick claude-fable-5-1 when you want its always-on thinking, which cannot be disabled, or up to 128000 output tokens in a single response. Pick gemini-3.8-flash for high-volume or cost-sensitive work, or when you need audio and video input, accepting its 65536 max output.

Benchmarks

LeadsAbove averageNo peer higherClaude Fable 5.1416 / 185 / 18Gemini 3.8 Flash212 / 165 / 16

6 measured on both.

Claude Fable 5.1 Gemini 3.8 Flash other models measured peer average no peer scored higher
DeepSWE 1.1
67.4%
73.7%
BioMysteryBench hard
N/A
no peer scored higher 56.5%
OSWorld 2.0 Partial score, batch tool enabled
N/A
59%
HealthBench Professional
58.1%
52.1%
Finance Agent v2
N/A
no peer scored higher 61.4%
Legal Agent Benchmark
N/A
10%
GPQA Diamond
93.7%
95.3%
AutomationBench
31.4%
N/A
CharXiv (RQ) no tools
N/A
no peer scored higher 86.2%

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

Pricing

Claude Fable 5.1 Gemini 3.8 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 catalog at build time; each model page carries the current card.

Where they sit - input price per 1M tokens across all 71 chat models on this billing unit (log scale)

Capabilities

Claude Fable 5.1 Gemini 3.8 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 Fable 5.1 Gemini 3.8 Flash
Input modalities text image text image audio video
Output modalities text text
Released 2026-09-01 2026-09-02
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 3.8 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 3.8 Flash passed · 3 sentences

When we enabled reasoning, the model began generating hidden "thinking tokens" to work through logic step-by-step before producing a final answer. Because AI vendors bill for every single token processed—visible or not—this internal deliberation dramatically inflated our billable volume per query. In short, while our total number of user requests remained flat, the cost per transaction doubled to buy higher accuracy on complex tasks.

out 705 tok (+624 thinking) latency 6.4 s

Instruction following (exactly three sentences - 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 3.8 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 best = max(best, cur) else: cur = 1 return best ``` ### Explanation of the Bug The original function counted equal-neighbor transitions rather than the actual items making up each run. Because `cur` started at `0` (and was reset to `0` on mismatches), every run was undercounted by one: single-element lists returned `0` because the loop never executed, and any subsequent run—including those ending at the last index—recorded a count of $N - 1$ instead of $N$. The fix handles empty input upfront, initializes both `cur` and `best` to `1` (since any single element forms a run of length 1), and resets `cur` to `1` whenever a new run begins.

out 1393 tok (+1149 thinking) latency 10.1 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 3.8 Flash passed · 5/5 fields, guidance null

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

out 3989 tok (+3932 thinking) latency 30.4 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 3.8 Flash passed · 120 words, 0 banned, 1 question

Why pay multiple model providers for the exact same output? Introducing Universal Prompt Cache, our latest API gateway capability engineered to cut compute expenses and drop inference latency. When your application makes a call, the gateway inspects a central memory layer before routing traffic to external LLMs. If an identical query was previously processed by OpenAI, Anthropic, or Mistral, our gateway returns that response immediately. This shared cache eliminates duplicate token fees and insulates your production apps from vendor rate limits or unexpected downtime. Developers can easily customize expiration settings, enforce strict data privacy controls, and configure invalidation logic across every endpoint. Stop wasting your budget on repeated queries. Enable prompt caching in your dashboard to accelerate your pipeline today.

out 3833 tok (+3688 thinking) latency 21.8 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-3.8-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 3.8 Flash?

Gemini 3.8 Flash is cheaper on input / 1m tokens ($0.75 vs $10, 13× 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 Fable 5.1 against Gemini 3.8 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 Fable 5.1 and Gemini 3.8 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.

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