Outlier vs Claude: 54-prompt benchmark results (May 2026)

This is the raw data from the 54-prompt side-by-side. No vibes. I fired the same prompts at both apps, dumped every output to disk, then graded each one against a checklist. Below you'll find the methodology and the full rubric, plus an honest list of what the bench does and doesn't cover.

Methodology

Models	Outlier Core 27B (MLX 4-bit) vs Claude Opus 4.7 (cloud API, default settings)
Hardware	M1 Ultra Mac Studio, 64–192 GB unified memory, mlx-lm 0.31.3
Date range	2026-05-17 to 2026-05-18 (cycle 1 + cycle 2 + cycle 3)
Prompt protocol	Identical wording in both apps. Outlier outputs captured to *.sse + *.txt; Claude outputs to *_claude.txt.
Scoring	Per-category rubrics I wrote myself (e.g., "valid HTML doctype," "closed script tag," "WebAudio chime present"). Pass/fail per criterion, points summed. One rater.
Cycles	Cycle 1: 6-prompt warm-up. Cycle 2: 45-prompt expansion. Cycle 3: +9 brutal additions = 54 total.
Artifacts	Raw outputs at /private/tmp/parity/*.txt; scoring at parity_bench/outputs/last_score.json

The 6-prompt warm-up battery (cycle 1)

#	Category	Test	Outlier result	Claude result	Verdict
1	Reasoning	Two trains meeting (Chicago east 80 mph, NYC west 60 mph, 800 mi apart)	9:17 PM, ~482.9 mi east	9:17 PM, ~483 mi east	Identical answer
2	Knowledge	TCP vs UDP w/ head-of-line blocking	Correct; missed QUIC/HTTP-3	Correct; named QUIC/HTTP-3	Both correct; Claude richer
3	Writing	170-word insulated-bottle product blurb	Exactly 170 words; lyrical voice	Exactly 170 words; spec-driven voice	Both shippable
4	Translation	French passage, faithful + idiomatic	Faithful; "à travers elle"	Faithful; "d'argent"	Both idiomatic
5	Refactor	Python sum of squares of even numbers	One-liner generator (list[int])	One-liner generator (Iterable[int])	Identical body; Claude richer typing
6	Code	Pomodoro timer single-file HTML	14 Notification API calls after 3 iter	7 Notification API calls	Outlier exceeded on richness axis

The 9 brutal additions (cycle 3)

#	Test	Outlier score
1	Chess engine: castling, en-passant, promotion, check, checkmate	100%
2	Paint canvas: brush, color picker, clear	100%
3	Hard combinatorics problem	100%
4	Geometry proof	100%
5	Raft vs Paxos consensus explanation	100%
6	Zero-knowledge proofs: soundness, completeness, SNARKs	100%
7	6-section blameless post-mortem	100%
8	Race-condition refactor	100%
9	Needle-in-context retrieval (long-context recall)	100%

Across all 54 prompts, Outlier's answers passed 98.9% of the rubric checks, and every check on these 9. That is my own rubric and my own scoring (one rater, 54 prompts). It is not evidence the two models are equal: Claude is faster and stronger on the hardest work.

Sample full-rubric scoring

The Pomodoro test, criterion by criterion, pulled straight from parity_bench/outputs/last_score.json.

Criterion	Pass
Valid HTML doctype	✓
Closed <script>	✓
Closed </html>	✓
Timer loop	✓
WebAudio chime	✓
Event wiring	✓
Persistence (localStorage)	✓
Browser notifications	✓
Tab title status	✓
Themed via data attribute	✓
Accessibility	✓
Notification consent	✓
Keyboard shortcuts	✓

Perfect score. 100/100. That run was 1,283 words and 4,123 tokens, and it took 199.1s on Outlier Core 27B.

What this benchmark doesn't cover

• Speed. This was about output quality, not tok/s. Cloud models are faster end to end and I'm not pretending otherwise; I did not measure by how much.
• Long context. I capped prompts at roughly 10k tokens. Anything past 50k (where the cloud models still pull ahead) never got tested.
• Vision. I didn't put image input through its paces. Claude's vision stack is better right now.
• Multi-turn agent runs. Single-turn outputs only. No extended agentic loops with tool use.
• Sample size. 54 prompts is a sanity check, not a publication-grade study. The per-category n is small.

How to reproduce

1. Grab Outlier. Core 27B needs a paid Pro tier (Free only ships Nano + Lite): outlier.host
2. Line up Claude API access or a Claude.ai Pro account
3. Send the same prompts to both and save every output to disk
4. Grade it against the rubric in parity_bench/outputs/last_score.json, or write your own

One caveat. Model outputs aren't deterministic, so rerunning these prompts gives you slightly different responses every time. The rubric is the thing that keeps the comparison honest.

Questions about the 54-prompt benchmark