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Claude Fable 5.1 vs Qwen3.7 Plus

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 share a 1000000-token context and accept text and image, so the split is cost and control: claude-fable-5-1 costs 25x more on input and 31.25x more on output ($10/$50 per million versus $0.4/$1.6), and its thinking mode cannot be turned off. Pick claude-fable-5-1 when you want always-on reasoning plus up to 128000 output tokens in one pass; pick qwen3.7-plus for high-volume or cost-sensitive work, video input, or when you need to disable thinking, accepting 65536 max output and a $1.6 per-million thinking input rate.

Benchmarks

Above averageNo peer higherClaude Fable 5.17 / 77 / 7Qwen3.7 Plus7 / 125 / 12
Claude Fable 5.1 Qwen3.7 Plus other models measured peer average no peer scored higher
SWE-Bench Pro
N/A
57.6%
ScreenSpot-Pro
N/A
no peer scored higher 79%
GDPval-AA v2 Elo · 1711-1853 per Anthropic · 2026-09-05
no peer scored higher 1853
N/A
Humanity's Last Exam no tools
no peer scored higher 60.9%
34.7%
AutomationBench
no peer scored higher 31.4%
N/A
BabyVision
N/A
no peer scored higher 64.7%

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

Pricing

Claude Fable 5.1 Qwen3.7 Plus Δ
Input / 1M tokens $10 $0.4 25×
Output / 1M tokens $50 $1.6 31×
Cache read / 1M tokens $0.25 $0.08 3.1×
Cache write 1.25x (5m) / 2x (1h) 1.25x -

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 67 chat models on this billing unit (log scale)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

Capabilities

Claude Fable 5.1 Qwen3.7 Plus
Tool use yes yes
Thinking control always on configurable
Structured output yes yes
Prompt caching explicit (you mark the prefix) implicit + explicit
Cache lifetime 5m default, 1h option explicit: 5m, reset on hit
Minimum cached prefix 1024 tokens 1024 tokens

Specs

Claude Fable 5.1 Qwen3.7 Plus
Input modalities text image text image video
Output modalities text text
Released 2026-09-01 2026-06-01
Knowledge cutoff 2026-06 -
Context window 1M 1M
Max output 128K 66K
Thinking parameter output_config.effort (thinking is adaptive-only and always on)
  • enable_thinking
  • thinking_budget
  • preserve_thinking
Accepted values
effort
  • low
  • medium
  • high
  • xhigh
  • max
enable_thinking
  • true
  • false
thinking_budget
  • in tokens
preserve_thinking
  • true
  • false
Default

thinking always on (adaptive)

effort
  • high

on

the Qwen3.7 Plus series is hybrid thinking with thinking enabled by default, and preserve_thinking is off

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 · Qwen3.7 Plus

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="qwen3.7-plus",  # 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 Qwen3.7 Plus?

Qwen3.7 Plus is cheaper on input / 1m tokens ($0.4 vs $10, 25× 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 Qwen3.7 Plus 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 Qwen3.7 Plus 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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