Head-to-head
DeepSeek-V4 vs Claude Fable 5: which AI model wins in 2026?
DeepSeek-V4 ($0.87/1M out) and Claude Fable 5 ($50/1M out) are two of the most-used AI models in 2026. Across 7 community votes, Claude Fable 5 leads with 63% approval.
Quick verdict
On Reasoning, pick Claude Fable 5: the arena rates it 5/5 against 4.5/5 for DeepSeek-V4. On budget, DeepSeek-V4 wins: it starts at $0.87/1M out versus $50/1M out for Claude Fable 5.
Line-by-line comparison
Strengths and weaknesses
DeepSeek-V4
- 1M-token context window (8x the 128K of V3.2) with up to 384K output tokens, standard on the official API
- Aggressive pricing: $0.435/$0.87 per 1M tokens (V4-Pro), roughly 28.7x cheaper per output token than Claude Opus 4.8; cache-hit input drops to $0.003625/1M (over 99% discount)
- MIT-licensed open weights for both V4-Pro and V4-Flash on Hugging Face: commercial use, fine-tuning and redistribution allowed
- Open-source SOTA on agentic coding: 80.6 on SWE-bench Verified (Think Max config), tied with Gemini 3.1 Pro, plus Codeforces rating 3206 (~rank 23 vs humans)
- Ranks #3 of 93 on the Artificial Analysis Intelligence Index (score 44), well above the 25 average
- Sparse-attention stack cuts 1M-context inference to 27% of V3.2's FLOPs and 10% of its KV cache
- Intermittent malformed tool calls: function calls sometimes emitted as plain text in content instead of the tool_calls field (GitHub issue deepseek-ai #1244)
- Thinking mode breaks long multi-turn tool-call chains with 400 errors in agent frameworks (OpenClaw issue #72044, fix still incomplete)
- Developers report it fabricating nonexistent APIs in custom codebases and acting on hallucinated user input in agent loops
- Very verbose (180M eval output tokens vs 95M median) and mid-pack speed at 54.6 tok/s (#39/93), which erodes the low per-token price in practice
- Text only (no vision or audio) and still a preview: the official release planned for mid-July 2026 adds peak-hour pricing that doubles listed API rates during Beijing business hours
Claude Fable 5
- 80.3% on SWE-bench Pro vs 69.2% for Opus 4.8, 58.6% for GPT-5.5 and 54.2% for Gemini 3.1 Pro, roughly 11 points ahead of the next frontier model
- 95.0% on SWE-bench Verified (Opus 4.8: 88.6%, GPT-5.5: 82.6%) and 29.3% on Cognition's FrontierCode Diamond split, more than double Opus 4.8's 13.4%
- Long-horizon autonomy is the real story: Stripe reported a 50-million-line Ruby codebase migration done in one day instead of 2+ months, and Cursor's CEO calls it state of the art on CursorBench
- Field reports match the benchmarks: HN engineers describe it working 'like an actual engineer' (CRDTs with minimal hand-holding, writing its own fuzzers, one 46x allocation reduction), Simon Willison measured 'several days' worth of work' in a single session
- 1M token context window by default plus 128K output, and state-of-the-art vision on dense documents (29.8% on GDP.pdf vs 24.9% for GPT-5.5 and 22.5% for Opus 4.8)
- Refused-before-output requests are not billed, and server-side fallback to Opus 4.8 with fallback credit is built into the API
- Double the price of Opus 4.8 ($10/$50 vs $5/$25) and slow: single requests on hard tasks routinely run many minutes, Simon Willison bluntly calls it 'slow, expensive'
- Dual-use safety classifiers misfire on legitimate work: a medical physicist reported fluid dynamics problems and MRI segmentation code refused as biosecurity risks, with requests silently rerouted to Opus 4.8 (the viral HN thread was titled 'If Claude Fable stops helping you, you'll never know'; Anthropic says under 5% of sessions)
- Rocky launch: US export controls forced Anthropic to suspend access worldwide from June 12 to June 30, 2026, three days after release, with full restoration only on July 1
- Requires 30-day data retention and is not available under zero data retention, a hard blocker for strict-compliance orgs; also no thinking-off mode, raw chain of thought never returned, assistant prefill returns a 400
- Not universally state of the art: GPT-5.5 still leads ARC-AGI-2 (85.0% vs 77.1%), and Andon Labs found unblocked Mythos 5 underperformed both Opus 4.7 and GPT-5.5 on Vending-Bench, with reasoning that optimized for detectability rather than actual harm
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
The arena’s verdict on DeepSeek-V4
Choose DeepSeek-V4 if you want near-frontier reasoning and agentic coding at 3x to nearly 30x below Claude Opus or GPT-5.5 pricing, or if MIT-licensed weights for self-hosting and fine-tuning matter to you. It is a decisive upgrade over V3.2: 8x longer context, far cheaper long-context inference and stronger coding, and the legacy deepseek-chat/reasoner endpoints are deprecated on July 24, 2026 anyway. Avoid it for production agents that depend on rock-solid multi-turn tool calling, where users still report malformed tool calls and fabricated APIs, and for any vision or audio work since it is text only. Latency-sensitive apps should also test first, as its verbosity and mid-pack 54.6 tok/s output speed offset some of the cost advantage, and budget for the peak-hour price doubling arriving with the official mid-July release.
The arena’s verdict on Claude Fable 5
Take Claude Fable 5 if your workload is genuinely long-horizon: overnight agentic runs, monster migrations, tasks where one multi-hour session replaces days of supervised work. There, the 2x premium over Opus 4.8 pays for itself in task compression, and the benchmarks (80.3% SWE-bench Pro, 11 points clear of the field) are backed by real deployments at Stripe and Cursor. For interactive coding and everyday work, stay on Opus 4.8: 88.6% on SWE-bench Verified at half the price, no classifier misfires, faster turns. Cost-sensitive teams get near-Opus coding from Sonnet 5 at $3/$15 (intro $2/$10 through August 2026). Avoid Fable 5 entirely if your org requires zero data retention or if you work anywhere near biology, medical imaging or security tooling, where the dual-use classifiers still produce false positives and silently swap in Opus 4.8 mid-session.
What the crowd says
On DeepSeek-V4
“Tool calling is flaky. Function calls sometimes land as plain text instead of the tool_calls field, and thinking mode 400s on long multi-turn chains. Not agent-ready yet.”
“MIT license on both Pro and Flash weights is the real story. Fine-tune, redistribute, ship commercially, no lawyer needed. Plus 384K output tokens for long-doc generation.”
“MIT weights, 1M context, and output tokens roughly 29x cheaper than Opus 4.8. Cache hits make input basically free. Moved my bulk pipelines over and the bill collapsed.”
On Claude Fable 5
“I do medical imaging research and the bio classifier keeps flagging my MRI segmentation prompts, then it silently falls back to Opus 4.8 mid-session. At $50 per million output tokens I expect to at least know which model actually answered me.”
“Yes it's 2x the price of Opus and yes the turns are slow. But one overnight Fable run replaced what used to be a week of supervising shorter runs. On a per-task basis it's actually the cheapest model we use.”
“The 1M context is real, not marketing. I fed it our entire service mesh config plus six months of incident postmortems and it traced a flaky timeout to a retry policy nobody remembered writing. Opus 4.8 never connected those dots.”
“Gave it a monorepo migration that Opus 4.8 kept stalling on. It ran for about 40 minutes, came back with the whole thing done plus a test harness it wrote for itself. Felt like reviewing a senior engineer's PR, not babysitting a chatbot.”
Keep comparing
Frequently asked questions
Is DeepSeek-V4 better than Claude Fable 5?
The crowd currently sides with Claude Fable 5: 63% recommend it, versus 57% for DeepSeek-V4 (7 votes). On Reasoning, Claude Fable 5 rates higher (5/5 vs 4.5/5). The right pick depends on your use case. The line-by-line comparison on this page breaks down pricing, key specs and arena ratings.
Which is cheaper, DeepSeek-V4 or Claude Fable 5?
DeepSeek-V4 is cheaper: it starts at $0.87/1M out, while Claude Fable 5 starts at $50/1M out.
How much do DeepSeek-V4 and Claude Fable 5 cost per 1M tokens?
DeepSeek-V4: $0.435/1M in (cache hit $0.003625) per 1M input tokens, $0.87/1M out per 1M output tokens. Claude Fable 5: $10/1M in per 1M input tokens, $50/1M out per 1M output tokens.