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Claude Fable 5.1 vs DeepSeek V4 Pro (0813)

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 models share a 1,000,000-token context window, chat, code, tools and reasoning, so the split comes down to inputs and price: claude-fable-5-1 accepts text and image and always runs with thinking enabled, while deepseek-v4-pro-0813 is text-only but allows up to 393,216 output tokens against Claude's 128,000. On rates, deepseek-v4-pro-0813 is far cheaper at $1.32 input and $3.96 output versus $10 and $50, roughly 7.6x and 12.6x less. Pick claude-fable-5-1 when you need image input or built-in thinking; pick deepseek-v4-pro-0813 for long text generations at volume.

Benchmarks

Above averageNo peer higherClaude Fable 5.17 / 77 / 7DeepSeek V4 Pro (0813)11 / 131 / 13
Claude Fable 5.1 DeepSeek V4 Pro (0813) other models measured peer average no peer scored higher
Terminal-Bench 2.1
N/A
87.9%
OSWorld 2.0 partial
77.9%
N/A
Cybergym
N/A
83.3%
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%
42.7%
Agents' Last Exam
N/A
25.7%

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

Pricing

Claude Fable 5.1 DeepSeek V4 Pro (0813) Δ
Input / 1M tokens $10 $1.32 7.6×
Output / 1M tokens $50 $3.96 13×
Cache read / 1M tokens $0.25 $0.132 1.9×
Cache write 1.25x (5m) / 2x (1h) no separate charge -

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)

Capabilities

Claude Fable 5.1 DeepSeek V4 Pro (0813)
Tool use yes yes
Thinking control always on always on
Structured output yes yes
Prompt caching explicit (you mark the prefix) implicit (automatic)
Cache lifetime 5m default, 1h option no fixed TTL (evicted when unused)
Minimum cached prefix 1024 tokens not published

Specs

Claude Fable 5.1 DeepSeek V4 Pro (0813)
Input modalities text image text
Output modalities text text
Released 2026-09-01 2026-08-13
Knowledge cutoff 2026-06 -
Context window 1M 1M
Max output 128K 393K
Thinking parameter output_config.effort (thinking is adaptive-only and always on) reasoning_effort
Accepted values
effort
  • low
  • medium
  • high
  • xhigh
  • max
reasoning_effort
  • the model card documents low
  • high
  • 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 · DeepSeek V4 Pro (0813)

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="deepseek-v4-pro-0813",  # 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 DeepSeek V4 Pro (0813)?

DeepSeek V4 Pro (0813) is cheaper on input / 1m tokens ($1.32 vs $10, 7.6× 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 DeepSeek V4 Pro (0813) 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 DeepSeek V4 Pro (0813) 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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