New Sign up free, 10 calls on us. Up to $1, no card needed.

Claude Fable 5.1 vs Gemini 4 Argon

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

Both run near a million tokens of context (1000000 for claude-fable-5-1, 1048576 for gemini-4-argon), but the rate cards are far apart: gemini-4-argon is 5x cheaper on input ($2 against $10) and output ($10 against $50), and 2.5x cheaper on cache reads ($0.1 against $0.25). Pick gemini-4-argon when cost dominates or the input includes audio or video, which claude-fable-5-1 does not take. Pick claude-fable-5-1 for text and image work that needs replies up to 128000 tokens, about 2x the 65536 on gemini-4-argon.

Benchmarks

Gemini 4 Argon: the vendor has not published benchmark scores.

Above averageNo peer higherClaude Fable 5.119 / 213 / 21
Claude Fable 5.1 Gemini 4 Argon other models measured peer average ★ no peer scored higher
DeepSWE 1.1
67.4%
N/A
OSWorld 2.0 partial
80.7%
N/A
HealthBench Professional
58.1%
N/A
Terminal-Bench-Science 0.1
52.6%
N/A
GPQA Diamond
93.7%
N/A
AutomationBench
31.4%
N/A
Chartography with tools
88.4%
N/A

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

Pricing

Claude Fable 5.1 Gemini 4 Argon Δ
Input / 1M tokens $10 $2 5×
Output / 1M tokens $50 $10 5×
Cache read / 1M tokens $0.25 $0.1 2.5×
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 4 Argon
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 4 Argon
Input modalities text image text image audio video
Output modalities text text
Released 2026-09-01 2026-10-13
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 4 Argon

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 4 Argon passed · 3 sentences

Enabling the reasoning feature forces the AI to generate an extensive, hidden "chain of thought" to solve complex problems before delivering the final answer. Because LLM providers bill us per token generated, we are now paying for thousands of these invisible "thinking" words on top of the standard visible output. While this doubles our raw usage costs per query, it represents an investment in significantly higher accuracy and drastically reduced error rates for our most critical tasks.

out 91 tok 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 4 Argon passed · 8/8 cases

```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 ``` The bug occurs because the original function counts the number of matching adjacent *pairs* rather than the actual number of *elements* in a run, causing all non-empty results to be off by one. Because a single element constitutes a valid run of length 1, initializing and resetting `cur` and `best` to `0` instead of `1` means the first element of any sequence is never counted; this causes single-element lists (where the loop doesn't run) to wrongly return `0` and makes it seem like runs at the end are missed because a pair of matching elements only increments `cur` to `1` instead of `2`. […]

out 286 tok latency 7.5 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 4 Argon passed · 5/5 fields, guidance "withheld pending the Q4 close"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```

out 68 tok latency 5.8 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 4 Argon passed · 120 words, 0 banned, 1 question

Are you paying twice for the exact same AI responses? Today, we are introducing Cross-Provider Prompt Caching for our API gateway, a new feature designed to reduce your artificial intelligence infrastructure costs and latency. When your applications send identical prompts to different large language models, our gateway now intercepts the request and serves a verified, cached response in milliseconds. It works across OpenAI, Anthropic, and open-source models simultaneously. If your primary provider experiences an outage, your fallback model still benefits from the shared cache Layer, preventing expensive redundant computations. Engineering teams can configure custom time-to-live settings, set similarity thresholds, and monitor cache hit rates directly from the dashboard. Activate Cross-Provider Prompt Caching in your gateway settings today and start saving.

out 156 tok latency 6.3 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-4-argon",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

Get your API key →

FAQ

Which is cheaper, Claude Fable 5.1 or Gemini 4 Argon?

Gemini 4 Argon is cheaper on the "Input / 1M tokens" row ($2 vs $10, 5.0× 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 4 Argon 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 4 Argon 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.

Related comparisons

From our measured studies