A model in the suite · Moonshot AI

Kimi K3

Moonshot AI · Kimi · Moonshot kimi-k3 reasoning_effort=max (direct Chat Completions, streaming, render screenshots as vision input, harness Perplexity web_search accommodation) · 2026-07-18

77/100
Strict suite averageNo legacy score · 4 benchmarks

Kimi K3 / max thinking effort (only value the API accepts; thinking always on) / Suite 2.0 API harness, direct Moonshot Chat Completions / render screenshots fed back as vision input / web search via harness Perplexity tool (accommodation: Moonshot's own search tool unavailable at launch, 2026-07-16)

Copies Kimi K3's full data pack — paste it into ChatGPT, Claude, or any AI to talk it through.

How Kimi K3 handled each benchmark

Score, capability radar, and the honest read on what it nailed and where it slipped. Hit Overlay to drop other models onto the same axes.

Dingo & Co. Knowledge Work

A 23-deliverable consulting brief: research, financial reconciliation, regulatory analysis, decks and spreadsheets. Tests whether a model can run an entire knowledge-work engagement end to end.

92
Near Mastery

Near-mastery Dingo & Co. result. The run completed the full artifact set in real formats and, more importantly, showed unusually strong business judgment on the core traps: legality, ethics, market creation, Alaska/Australia mismatch, TAM honesty, attach-rate artifacts, and support-language liability. The main deductions are for minor mobile dashboard polish, research/source verification gaps that still require counsel or independent checking, and a few external-copy/source-cleanup issues. Overall, this is a highly usable VP-level work package with minimal human repair needed before internal board use.

OverlayDownload radar
Instr. FollowingArtifact ValiditySource IntegrityResearch GroundingSemantic JudgmentQuant. Reas.Visual StorytellingUX ReviewabilityProd. ReadinessSpeedSpatial Reas.
1GPT-5.6 Sol93
2Kimi K392
3Grok 4.591
4GPT-5.6 Luna90
5GPT-5.6 Terra88
6GLM 5.2 (OpenRouter)88
7Claude Fable 581
8Claude Sonnet 5 (xhigh)81
9Claude Opus 4.880
10GPT-5.578
11Gemini 3.5 Flash (High) Fast62
12Opus 4.754
13Sonnet 4.652
14Gemini 3.1 Pro38

What it nailed

  • Exceptionally strong handling of the benchmark's core absurdities: dingo welfare, Alaska/Australia mismatch, import-created demand, legality, support-language liability, and ethics.
  • Comprehensive artifact completion in real requested formats, including substantial DOCX/PPTX/XLSX/PDF/HTML outputs.
  • Consistent canon-number system prevents the usual drift across deck, summary, GTM plan, dashboard, spreadsheets, and FAQ.
  • Strong quantitative reconciliation of revenue, units, budget, runway, CAC/LTV, NPS, import attach-rate artifacts, and import-margin uncertainty.
  • Fictional scenario competitors are labeled instead of being hallucinated as real market evidence.

Where it slipped

  • Minor mobile dashboard navigation/header clipping and very small header/logo text were observed by one visual reviewer.
  • Some research claims still need independent verification before external use; Australian export legality is explicitly left as an open official-source gap.
  • A few citation/source-count details are imperfect, including source-count wording and at least some source-link/citation polish concerns.
  • Some public-facing copy is very good but still requires counsel review because the product category and import program are legally sensitive.
  • Quote-permission and some validation/manifest claims are model-authored assertions unless checked against the original source files.
Wall clock 12m 46s

Car Wash Operations

A filthy operational dataset — ghost records, orphaned orders, typo'd customers, raw enum variants. Tests judgment under messy real-world data: what gets fixed, quarantined, or wrongly promoted.

65
Competent Scaffold

A broad and polished migration package with a real database, strong static audit UI, extensive provenance scaffolding, and many correct canary detections. However, it fails a central planted ghost/test obstacle: evidence indicates the Mickey Mouse magic-payment record remained in canonical payment/revenue data instead of being quarantined. Combined with weak image OCR handling and some entity-resolution anomalies, this is a competent reviewer scaffold rather than a production-clean migration.

OverlayDownload radar
Instr. FollowingArtifact ValiditySource IntegritySemantic JudgmentQuant. Reas.UX ReviewabilityProd. ReadinessSpeed
1Claude Fable 588
2Claude Opus 4.886
3Kimi K365
4Claude Sonnet 5 (xhigh)64
5GPT-5.6 Sol55
6GPT-5.6 Terra55
7GPT-5.555
8GPT-5.6 Luna55
9Grok 4.555
10GLM 5.2 (OpenRouter)55
11Gemini 3.5 Flash (High) Fast51
12GPT-5.451
13Opus 4.748

What it nailed

  • Substantial artifact set: SQLite database, deterministic migration script, static audit UI, frontend JSON/JS export, design notes, README, and migration report.
  • Strong reviewer UX with clean desktop/mobile rendering and no visual defects reported by independent visual judges.
  • Good coverage of many structured-source problems: source inventory, SVC-007 conflict, corrupted JSON partial recovery, duplicate images, status/payment normalization, price eras, and many typo/nickname merges.
  • Useful provenance/review architecture with source_files, source_records, conflicts, rejected items, review flags, and provenance explorer.

Where it slipped

  • Major canary failure: at least one obvious ghost/test record, Mickey Mouse with magic payment, appears to be retained as canonical payment/revenue rather than quarantined.
  • Rejected-items evidence only covers Test Customer rows; there is no clear evidence that Mickey Mouse or Asdf Asdf were rejected.
  • Handwritten receipt/image extraction is weak: several important images had no usable OCR and image-derived price/customer conflicts are only partially represented.
  • Entity resolution is imperfect, with visible duplicate identities such as Amber and Amber Bilbow sharing contact evidence.
  • Department-code normalization and full referential-integrity validation are not independently demonstrated.
Promotes Ghost Records
Wall clock 59m 23s

Brick — The AI LEGO Build

68
Competent Scaffold

Equal-weight mean of four isolated Brick case scores: 100-piece-lunar-rover=82.5, 250-piece-rescue-helicopter=84.0, 500-piece-cyberpunk-food-stall=55.0, 1000-piece-airship-research-station=52 -> 68.38.

OverlayDownload radar
Instr. FollowingArtifact ValiditySource IntegritySemantic JudgmentQuant. Reas.Spatial Reas.Visual StorytellingUX ReviewabilityProd. ReadinessSpeed
1Claude Fable 588
2Claude Opus 4.882
3Claude Sonnet 5 (xhigh)78
4Kimi K368
5GPT-5.6 Sol59
6Gemini 3.5 Flash (High) Fast56
7Grok 4.555
8GPT-5.6 Terra54
9GPT-5.6 Luna50
10GLM 5.2 (OpenRouter)50

What it nailed

  • [100-piece-lunar-rover] Exactly 100 measured parts for the isolated lunar-rover case.
  • [100-piece-lunar-rover] Strong single-spec structure: kitSpec drives the rendered model, UI manifest, and step-based assembly guide.
  • [100-piece-lunar-rover] Completed rover is visually recognizable and recordable on desktop, with clear lunar-rover features.
  • [100-piece-lunar-rover] Connectivity is mostly successful for a 100-piece build: 91 of 100 parts are in the largest strict component and floating_fraction is only 0.09.
  • [250-piece-rescue-helicopter] Produced the required isolated case artifact with a structured kitSpec and a complete browser-based assembly guide.
  • [250-piece-rescue-helicopter] Exact 250-piece count verified by independent extraction from the HTML/spec.
  • [250-piece-rescue-helicopter] Connectivity is strong for this benchmark scale: only 5.6% floating under the strict mechanical validator.
  • [250-piece-rescue-helicopter] Clear step/chapter organization and a recognizable rescue-helicopter silhouette with landing pad, rotors, skids, door, and winch.
  • [250-piece-rescue-helicopter] Desktop visual presentation is polished and recording-friendly.
  • [500-piece-cyberpunk-food-stall] Delivers the required isolated case with artifacts/index.html and a separate kitSpec.json.
  • [500-piece-cyberpunk-food-stall] Exact 500-piece declared kit with structured part records, step IDs, dimensions, positions, and rotations.
  • [500-piece-cyberpunk-food-stall] Strong cyberpunk food-stall concept with recognizable stall silhouette, neon signage, kitchen/counter details, accessories, and delivery scooter.
  • [500-piece-cyberpunk-food-stall] Animated Three.js guide includes the expected reviewer controls and a polished, recording-friendly visual style.
  • [500-piece-cyberpunk-food-stall] Desktop visual quality is strong; operator findings mark only minor issues for this case.
  • [1000-piece-airship-research-station] Delivered the required artifacts/index.html for the isolated 1000-piece case, plus kitSpec.json and a build guide.
  • [1000-piece-airship-research-station] Declared and measured part counts match at 1048 pieces, within the requested range.
  • [1000-piece-airship-research-station] The kit is structured around a machine-readable kitSpec with chapters, steps, part IDs, and per-part geometry.
  • [1000-piece-airship-research-station] Concept coverage is rich: mountain base, research station, gantry, airship envelope, gondola, cargo pod, instruments, and final docking sequence.

Where it slipped

  • [100-piece-lunar-rover] Independent connectivity contradicts the model's self-validation claim: 9 parts are floating under strict stud/connector coupling.
  • [100-piece-lunar-rover] High near_miss_count indicates spatial placements are often approximate rather than cleanly engaged on a strict stud grid.
  • [100-piece-lunar-rover] Several small accessories such as control, windshield, lamp clips, antenna/sensor, and dish are not mechanically validated as attached.
  • [100-piece-lunar-rover] Mobile completed view is crowded, with overlays and controls obscuring part of the model and parts-added list.
  • [100-piece-lunar-rover] The viewer depends on CDN-loaded Three.js rather than being fully offline self-contained.
  • [250-piece-rescue-helicopter] Build data integrity is limited by the validator contradicting the artifact's own support/connectivity claims.
  • [250-piece-rescue-helicopter] 801 near misses is high relative to 250 parts, suggesting approximate placement rather than consistently strict stud-grid engagement.
  • [250-piece-rescue-helicopter] Fourteen parts remain strictly floating, including several small accessories and rotor/chain-related pieces.
  • [250-piece-rescue-helicopter] Mobile completed-state presentation has minor overlay/cropping defects.
  • [500-piece-cyberpunk-food-stall] Mechanical connectivity validator could not extract or measure the model, leaving strict stud-coupling buildability unverified.
  • [500-piece-cyberpunk-food-stall] Self-reported validation claims cannot substitute for independent physical measurement.
  • [500-piece-cyberpunk-food-stall] Runtime kitSpec was not discoverable by the provided validator from index.html, reducing confidence in automated build verification.
  • [500-piece-cyberpunk-food-stall] Mobile presentation has minor crowding/overlap/cropping issues in completed-state views.
  • [500-piece-cyberpunk-food-stall] Very slow generation run: 26 API calls and roughly 7,256 seconds wall-clock time.
  • [1000-piece-airship-research-station] Strict mechanical validation shows 35.78% of parts floating and 252 disconnected components.
  • [1000-piece-airship-research-station] The near_miss_count of 6512 indicates widespread approximate placement rather than reliable stud-grid engagement.
  • [1000-piece-airship-research-station] Rendered completed-state evidence is blocked by the intro modal, so the actual 1000-piece completed model cannot be visually reviewed.
  • [1000-piece-airship-research-station] Controls are present in the HTML but not independently verified as usable because the operator screenshots never reach the visualizer.
  • [1000-piece-airship-research-station] Self-reported validation claims are contradicted by operator visual findings and are not treated as evidence.
Physical Buildability UnverifiedPhysics UnverifiableOperator Blocking DefectOperator Major Primary ArtifactOperator Multiple Major DefectsSevere Physical Implausibility
Wall clock 4h 59m 17s

Artemis II Mission Visualization

83
Excellent

Excellent, broad Artemis II visualization package with strong mission-specific storytelling, a runnable-looking 3D interface, complete deliverables, and polished desktop presentation. It is not autonomous publication-grade because the central factual/source claims were not independently validated in the packet, and the mobile UI plus minor loading-overlay issue need polish before client-facing use.

OverlayDownload radar
Instr. FollowingArtifact ValiditySource IntegrityResearch GroundingSemantic JudgmentQuant. Reas.Spatial Reas.Visual StorytellingUX ReviewabilityProd. ReadinessSpeed
1GPT-5.6 Sol89
2GPT-5.6 Luna87
3Claude Fable 586
4GPT-5.6 Terra86
5Kimi K383
6GPT-5.579
7Grok 4.579
8Claude Opus 4.876
9Claude Sonnet 5 (xhigh)71
10Opus 4.760
11GLM 5.2 (OpenRouter)58
12Gemini 3.5 Flash (High) Fast54

What it nailed

  • Complete artifact package with fact sheet, interactive visualization, source list, documentation, shot links, and screenshots.
  • Visualization renders successfully and presents a mission-specific 3D scene rather than a generic orbit shell.
  • Strong mission beat coverage from launch through recovery, including crew and vehicle context.
  • Good citation discipline in the artifacts, with bracketed source tags and explicit planned/as-flown labeling.
  • Useful production features such as deep links, camera presets, event stepping, HUD telemetry, and hide-UI recording mode.

Where it slipped

  • No independent source URL/content validation is provided for highly specific as-flown and post-mission claims.
  • Some key research assertions depend on the artifact's own verification claims and secondary sources, so publication would require manual fact checking.
  • Desktop render has a minor persistent loading-overlay concern.
  • Mobile view is crowded, with small or truncated timeline labels and overlays covering much of the scene.
  • The visualization depends on an external Three.js CDN for first load, so it is not fully offline/self-contained.
  • Run speed was very poor, with roughly 9 hours wall-clock time, 50 API calls, and 12 retries.
Wall clock 9h 8m 21s