Model Profile · Round 01
DeepSeek v4 Pro
DeepSeek · open weights · 128K ctx
Strong tone consistency at fraction of Opus pricing.
Composite Score
69.0
/100 · canonical
Arena ELO (R1)
—
joined post-R01
Multi-Turn ELO (R2)
1564
±101 · n=31
Reliability Rank
#5
avg 5.8
▌ Section 02 · The Lede
What this model is for.
DeepSeek's v4 Pro lands as the best open-weight model in Round 02's multi-turn arena. It sits #3 on the multi-turn ELO at 1564, behind the two Opus models — and at $3.20 per million tokens, it's an order of magnitude cheaper than either. Strengths cluster on tone consistency (4.70/5, top of the pool) and reliability (avg failure rank 5.75 across the 20 models). The data is thinner than the Anthropic entries — n=31 multi-turn votes — and the Likert-judge mean is dragged down by a higher-than-expected fatal-flaw count (0.50 per session), but the multi-turn community votes have it slotted firmly in the upper third. If you can't deploy Anthropic for compliance reasons, this is the open-weight read.
▌ Section 03 · At a Glance
Cross-test position
DeepSeek v4 Pro holds #3 in Multi-Turn and #3 in Rubric. Sits at #15 on Cost · Latency — the caveat to watch.
▌ Section 04 · Strength & Weakness
Where it shines. Where it stumbles.
▲ Strength
Strong tone consistency (4.70/5, top of pool). Top-5 on multi-turn ELO (1564). $3.20/1M, a tenth of Opus pricing.
▼ Weakness
0.50 fatal-flaws per session is mid-pack, not top-tier. Missing F3 (lore) and F8 (momentum) adversarial coverage in upstream — those axes are blank cells on the model profile page.
▌ Section 05 · Failure Modes
Per-axis breakdown.
Six adversarial probes per session, twenty sessions per model, judged by Sonnet 4 against a fixed rubric. Higher score = the model handled the failure mode better. Bars below show the mean across sessions; the black tick marks the population mean (4.20). Right column shows mean and rank within the rp-bench pool.
▌ Coverage: 4/6F3 · Lore · F8 · Momentum not yet run on this model. Upstream rolls these out incrementally as new models join the pool.
F1 · Agency
Doesn't write your character's actions
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F2 · POV / Tense
Holds 2nd-person, present-tense narration
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F3 · Lore
not yet run on this model
—
—
F8 · Momentum
not yet run on this model
—
—
F12 · Instruction Drift
Keeps to the system prompt
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F13 · Context Attention
Holds character cards 50+ turns deep
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“Best open-weight in Round 02. Anthropic-tier reliability without the Anthropic price tag.”
— Round 02 verdict · Open-weight lead
▌ Section 06 · Subjective Dimensions
Engagement · Voice · Collaboration.
All three dimensions scored 1–5 by Sonnet 4 LLM-judge across twenty 12-turn multi-turn sessions. The same battery feeds the failure-mode rubric above — these are the subjective half of that judgment.
▌ Section 07 · Behavioral Metrics
How it writes.
Quantitative signals from the same 20 multi-turn sessions, compared against the population mean across all 11 models.
Avg words / turn
259↓
pop avg 265 · -2%
Unique-word ratio
0.664↑
pop avg 0.655 · +1%
Repetition score
0.040↓
pop avg 0.049 · -18%
▌ Section 08 · Flaw Hunter
Adversarial probe score.
Score of 100 minus deductions across 22 fail-mode flag types on adversarial 12-turn sessions. Higher = fewer flaws caught. Population range across the round is 12.8–46.9.
▌ Score breakdown
Mean 19.4
Median 46.5
Fatal/sess 0.50
Major/sess 9.00
▌ Top flaws caught
purple_proserecycled_descriptionnarrating_emotions
▌ Section 09 · Sample Responses
Highest- and lowest-rated turns.
▌ Pending Round 02
Best- and worst-rated sample responses ship with the raw-vote endpoint in Round 02. When that lands, this section will surface the model’s highest- and lowest-scoring blind-arena turns side by side, scored on the same rubric the leaderboard uses.
▌ Round 01 verdict
DeepSeek v4 Pro is the model to pick if open weights are a hard requirement. The Round 02 sample is thin (n=31), but every axis with data points top-third. The blanks on F3 and F8 are real — if your scenes lean on lore consistency or narrative momentum, run a few sessions of your own before committing. For most cases, this is a credible Sonnet alternative.
▌ Section 10 · Compare & Drill
Stack it against another model.
Profile · DeepSeek v4 Pro · Round 01