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

Claude Fable 5.1 vs DeepSeek V4 Flash (0731)

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 1,000,000-token context window, so the split is cost and modality: claude-fable-5-1 costs $10 per million input and $50 per million output, roughly 23x and 38x the $0.44 and $1.32 of deepseek-v4-flash-0731. Pick claude-fable-5-1 when you need image input alongside text or always-on thinking, which cannot be disabled on it. Pick deepseek-v4-flash-0731 for text-only chat, code, tools and reasoning at high volume, or when a single reply must run long - its 393216-token max output is about 3x the 128000 tokens allowed on claude-fable-5-1.

Benchmarks

LeadsAbove averageNo peer higherClaude Fable 5.1519 / 213 / 21DeepSeek V4 Flash (0731)010 / 280 / 28

5 measured on both.

Claude Fable 5.1 DeepSeek V4 Flash (0731) other models measured peer average ★ no peer scored higher
DeepSWE 1.1
67.4%
54.4%
OSWorld 2.0 partial
80.7%
N/A
Cybergym
N/A
76.7%
HealthBench Professional
58.1%
N/A
JobBench
N/A
41.3%
GPQA Diamond
93.7%
89.9%
Agents' Last Exam
N/A
25.2%
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 DeepSeek V4 Flash (0731) Δ
Input / 1M tokens $10 $0.44 23×
Output / 1M tokens $50 $1.32 38×
Cache read / 1M tokens $0.25 $0.044 5.7×
Cache write 1.25x (5m) / 2x (1h) no separate charge -

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 DeepSeek V4 Flash (0731)
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 (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 Flash (0731)
Input modalities text image text
Output modalities text text
Released 2026-09-01 2026-07-31
Knowledge cutoff 2026-06 -
Context window 1M 1M
Max output 128K 393K
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 · DeepSeek V4 Flash (0731)

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

DeepSeek V4 Flash (0731) passed · 3 sentences

Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.

out 365 tok (+266 thinking) latency 5.1 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

DeepSeek V4 Flash (0731) 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 original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]

out 1268 tok (+1016 thinking) latency 14.0 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

DeepSeek V4 Flash (0731) passed · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }

out 1119 tok (+1072 thinking) latency 12.3 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

DeepSeek V4 Flash (0731) missed · 141 words, 0 banned, 1 question

**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]

out 254 tok (+80 thinking) latency 4.4 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="deepseek-v4-flash-0731",  # 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 DeepSeek V4 Flash (0731)?

DeepSeek V4 Flash (0731) is cheaper on the "Input / 1M tokens" row ($0.44 vs $10, 23× 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 DeepSeek V4 Flash (0731) 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 DeepSeek V4 Flash (0731) 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