Methodology

Different jobs need
different evidence.

Best Model Now does not reuse one universal leaderboard. Each category changes what we assess and which evidence is relevant. Methodology updated October 29, 2026.

Clear recommendations, with uncertainty in view.

Our research agent brings together evidence and evaluates the eligible contenders. A published ranking must pass its category’s evidence checks. Incomplete Builder consensus, inherited version placements, and rankings with nonspecific published evidence are labeled “Provisional: evidence still developing.” That label applies to the specific result, not to every category.

“Site-wide source activity” shows the latest completed source-check pass, which may cover only part of the site. Each category separately shows its last documented review, assessment publication, ranking-order change, original observation dates, and coverage of currently eligible contenders. Dates use Pacific time. A documented no-change review can advance that category’s next review deadline without making old evidence new. Missing comparisons remain visible and do not establish a model’s quality or placement.

Scored rankings and editorial recommendations

Writing and technical Coding use five practical factors. Builders use completed-product proof, practitioner consensus, ownership and publishing, and ease and value. Image and Video use the separate editorial ranking procedure: category judgments and explicit tiebreakers, without numerical scores. The percentages below do not apply to current media reviews.

Builder columns correspond to working-product proof, creator verdicts, ownership/publishing, and ease/value respectively.
ProfileUsed forPerformancePreferenceAccessValueRestrictions
BalancedWriting and technical Coding50%20%15%10%5%
Practical ConsensusWeb App, Mobile App, and Website Builders20%60%10%10%0%

What a close score means

A recommendation score combines craft or task performance with practical factors. It is not a percentage of quality, a probability, or a measure of statistical certainty. A model can earn more blind-test points and still place lower after access, preference, value, and restrictions are weighted. Small differences should be treated as close calls. The score alone does not establish a meaningful quality gap.

Evidence follows the actual task.

Writing
Writing quality, instruction control, revision strength, coherence, and human judgment. Fiction Prose and Screenwriting use dedicated blind craft tests as first-party evidence.
Coding
Technical categories use benchmarks and repository-level system tests for the 50% performance factor. Practical Consensus can inform only the 20% real-world preference factor; access, value, and restrictions retain their separate weights. Builder categories use Practical Consensus as their primary profile because finished products, iteration, ownership, and publishing matter more than isolated code generation.
Image
Blind preference, edit preservation, photographic style fit, control, and specialist visual judgment. Each award uses its own editorial rubric; Artistry prioritizes artistic impact rather than price.
Video
Motion coherence, temporal stability, direction, reference preservation, and demonstrated production functions. Generation, editing, capabilities, and the overall recommendation use distinct editorial rubrics, not numerical weights.

We rank the thing a person can actually use.

A pure model can be ranked by itself. A coding system or builder platform is different: its harness, tools, routing, plan, and release can materially change results. We therefore record tested configurations such as Codex + GPT-6 Astra.

When a platform chooses models behind the scenes, the public ranking says Platform-selected AI. That means the service may route a task to one model or several, and the exact stack may change. We rank the observable product configuration, record its version, plan, and test date, and do not pretend a single disclosed model powers it.

A new version is a separate contender and does not automatically earn its predecessor’s scores. Existing inherited placements remain provisional until version-specific comparative evidence is documented. An older model version remains eligible while it is publicly available and materially competitive. A successor does not retire it automatically, and two versions from the same family may both place when the evidence supports them.

A new Builder enters the active contender field only after first-party facts clear the eligibility gate and either qualifying hands-on practitioner evidence or repeated current comparison coverage establishes it as a serious option. A new marketing page alone is not enough.

Objective evidence

Benchmarks, arenas, structured leaderboards, and reproducible datasets. They must map meaningfully to the category.

Specialist evidence

Qualified creators and practitioners who show their work, test relevant tasks, and disclose enough context to judge the finding.

BMN testing

Controlled first-party test suites. Prompts, candidate access, outputs, blind rankings, reviewer identity, settings, costs, and dates are recorded. An incomplete suite cannot influence a published ranking.

Useful agreement, grounded in finished work.

Builder awards aggregate the current judgment of practitioners who have actually compared products. A qualifying creator may supply one, two, or three explicit choices, worth five, three, and one points in the order given. We never invent an unstated second or third choice. The vote is then combined with evidence that the tools produce functioning end products and provide a viable ownership and publishing path.

  1. Direct recent comparisons showing completed products receive full weight; detailed hands-on comparisons receive 85%, and established long-term use receives 70%.
  2. Evidence is time-weighted: full through 90 days, 80% through 180 days, 50% through one year, and excluded after that unless reconfirmed.
  3. Disclosed sponsored comparisons can contribute at 50%. Affiliate links and free access are discounted. Ongoing vendor relationships and undisclosed conflicts are excluded.
  4. A definitive winner needs three qualifying creator verdicts, two independent first-place votes, a demonstrated functioning product, and no unresolved availability, ownership, capability, or publishing issue.
Practical Consensus weights
SignalWeight
Creator verdicts60%
Working product proof20%
Ownership & publishing10%
Ease, iteration & value10%

How successfully can a nondeveloper build, launch, and maintain a working product?

Our builder recommendations are for people who understand AI but are not coding experts. We judge the effort required throughout the life of a project.

Build
How much technical knowledge, manual setup, or outside help is needed to turn an idea into a working product?
Revise
Can the user describe changes naturally and get the intended result without breaking existing features?
Recover
When something fails, can the system diagnose and repair it without requiring the user to understand code?
Operate
How easy are publishing, managing data, restoring earlier versions, and understanding ongoing costs?

These questions guide the existing evidence and scoring: working products and successful repairs, relevant practitioner comparisons, ownership and publishing, and ease of use and value. Hiding code earns no credit by itself; reducing the expertise needed while delivering reliable results does.

We identify the complete setup, including the interface, model, tools, hosting, and plan. For example, ChatGPT Work + Sites with a specified model is a different builder experience from using Codex in a terminal. Evidence for one does not automatically establish the performance of the other. Our own experience can support a review, but independent corroboration is still required.

A reusable signal, not a universal replacement

Practical Consensus is primary for Builders. In creative writing it corroborates blind judging and owner review; in image and video it strengthens workflow and practitioner-preference evidence; and in technical coding it is confined to the 20% preference factor. The remaining 80% still comes from technical performance, workflow, value, and restrictions.

Current evidence can change the recommendation now.

Technical Coding can receive an * Early evidence — position may change ranking as soon as current external benchmarks and original hands-on comparisons support it. Writing approvals apply only to Fiction Prose and Screenwriting; they do not hold up Coding, Image, Video or Builder reviews.

We identify the model, reasoning effort and coding tools, review the full eligible field, and require recent inspectable comparisons from at least two qualified publishers, including an objective benchmark. Different effort levels and harnesses remain separate observations. Provider claims alone cannot promote a model, and a creator’s articles and videos count as one publisher. Sponsorship and pre-release access are disclosed when known.

The five-factor coding rubric still guides the editorial decision. These factors are judgments informed by evidence, not measured benchmark percentages; we hide the aggregate score on early recommendations to avoid false precision. We explain conflicting findings and what could change the order, then reassess within two days. A benchmark win in code generation does not automatically prove superiority in debugging, refactoring or autonomous work.

Compare the task and the tested version.

We review published blind comparisons and hands-on tests from their original publishers. Every useful observation identifies the task, model version, settings when disclosed, result, and limitations. Sunburst and Flare, base and post-trained models, and 720p and 1080p tests are not interchangeable.

Preference leaderboards show what voters preferred in that test. They do not establish a universal winner for photographic realism, artistic taste, or complex filmmaking. Provider documentation establishes capabilities and access; it does not count as an independent quality comparison. Conflicting findings stay visible, and multiple articles or videos from one publisher count as one source.

New evidence since this ranking contains observations reviewed after the recommendation’s verification date. They may support or challenge it. Adding evidence does not automatically change the order, score, or verification date: those require a documented category reassessment. Rankings with generic or incomplete comparative support remain provisional.

New challengers. Two fresh scenes.

New challengers that pass a preliminary screen face the current top three in two equally weighted blind rounds. Every participant writes both scenes; there is no elimination between rounds. The owner approves fresh assignments specifying genre, approximate word count, premise and screenplay format. Prompts do not prescribe craft tricks or specific plot devices. Once both rounds have outputs, three AI judges independently rank the anonymous scripts with a different presentation order for each judge. The panel uses the three published Screenwriting leaders fixed for the batch; an already-started panel is preserved. Judges assess filmable action, dialogue and subtext, structure and pacing, originality, and screenplay format. The owner may add two distinct judges for both rounds, preserving the original votes. All selected judges count equally and must finish. The owner can accept or adjust either round’s order before revealing model identities. AI judges can favor familiar styles, including their own, so anonymity does not eliminate every bias.

Both rounds have equal weight, awarding one point per position from last to first. Panel ties use closeness to the requested word count; combined ties use average percentage deviation across both word targets. Remaining ties require an owner decision. Publication is a separate owner confirmation and retains the previous ranking for rollback. Two scenes are a limited sample, not proof of performance across every genre or an entire feature screenplay. Earlier tests below retain their original methodology.

Recorded test field and results

Screenwriting — New challengers · Version 1 · Completed 2026-10-26. 4 contenders. Scores below are test points. Challenger rounds use their combined ordinal result; historical tests used the earlier weighted methodology.

Recorded model identities and settings. New challenger rounds keep the same field; historical tests could eliminate contenders.
ContenderRecorded setting / accessRound 1Round 2Points
GPT-6 AstraProvider default · OpenAI API · OpenAI · gpt-6-astra217 / 8
GPT-6.1 SolProvider default · OpenAI API · OpenAI · gpt-6.1-sol127 / 8
GPT-6 SolProvider default · OpenAI API · OpenAI · gpt-6-sol334 / 8
Kimi K3.1 ProProvider default · maximum 32768 output tokens (including reasoning) · OpenRouter API · Moonshot AI · moonshotai/kimi-k3.1-pro442 / 8

Two original stories. The same competitors.

New qualifying challengers face the current top three on two fresh prompts in different genres. Assignments specify genre, approximately 1,000 words, premise, POV and tense, without craft instructions. All participants write in both rounds. Three AI judges independently rank the anonymous fiction drafts, with a different presentation order for each judge. The panel uses the three published Fiction Prose leaders fixed at the start of the batch; an already-started panel is preserved. The owner may add two distinct models to judge both rounds, expanding the panel to five while preserving the original votes. Each complete ballot has equal weight, and all selected judges must finish before the expanded result can be confirmed. The owner reviews the recommendation, can change either round’s order, and confirms both rounds before identities are revealed. AI judges can favor familiar styles, including their own, so anonymity does not eliminate every bias.

Both rounds have equal weight. Panel ties use closeness to the prompt’s word target; combined ties use average percentage length deviation across both rounds. Remaining ties require an explicit owner decision. Publication is a separate owner confirmation and retains the previous ranking for rollback. These provisional results describe this limited sample, not universal superiority across novels or genres. Earlier qualifier-and-revision tests remain preserved as history.

Recorded test field and results

Fiction Prose — New challengers · Version 1 · Completed 2026-10-27. 4 contenders. Scores below are test points. Challenger rounds use their combined ordinal result; historical tests used the earlier weighted methodology.

Recorded model identities and settings. New challenger rounds keep the same field; historical tests could eliminate contenders.
ContenderRecorded setting / accessRound 1Round 2Points
Claude Opus 5Provider default · maximum 32768 output tokens (including reasoning) · OpenRouter API · Anthropic · anthropic/claude-opus-5127 / 8
Claude Opus 5.5Provider default · maximum 32768 output tokens (including reasoning) · OpenRouter API · Anthropic · anthropic/claude-opus-5.5316 / 8
Kimi K3.1 ProProvider default · Moonshot AI MXFP4 · October 23 checkpoint · provider-default Max reasoning · maximum 32768 output tokens (including reasoning) · OpenRouter API · Moonshot AI · moonshotai/kimi-k3.1-pro244 / 8
Claude Fable 5.1Provider default · maximum 16384 output tokens (including reasoning) · OpenRouter API · Anthropic · anthropic/claude-fable-5.1433 / 8

A winner, a runner-up, and a third choice.

BMN publishes an editorial first, second and third place. We make a recommendation from the available evidence, explain why each option earned its place, and state how strongly that order is supported. These are judgments you can inspect, not invented numerical quality scores or claims of universal superiority.

We assess the complete eligible field against the category's primary criteria. When quality is effectively tied, we compare consistency, instruction fidelity, creative control, and breadth, in that order. If these still do not separate the contenders, we make a clearly identified editorial call and explain the deciding tradeoff. Each review states why first beats second, why second beats third, and why the strongest excluded challenger missed the podium.

What winning means in each category
CategoryPrimary judgment
Best Image OverallImage quality across tasks, followed by control, editing reliability, consistency, access and value.
Image EditingEdit precision, preservation of unchanged regions, reference identity and instruction fidelity.
PhotorealismPhotographic plausibility within the requested photo type, across casual phone and professional photography; polish alone earns no advantage.
ArtistryArtistic impact, composition, lighting, stylistic expression and range.
Best Video OverallVideo quality across tasks, followed by control, consistency, workflow, access and value.
Text-to-VideoOverall visual realization, believable motion and physics, temporal coherence, composition and meaningful realization of text-only prompts. Assess these together across representative scenes and best usable configurations; literal camera adherence alone does not decide overall quality. Price, access, speed and audio preference are separate.
Image-to-VideoRendered visual quality: believable motion and physics, source identity and composition, temporal stability, detail and instruction fidelity. Assess single-image and multi-reference work separately. Price, speed, access and audio preference do not determine this order.
Video-to-Video EditingRequested footage changes, natural skin and facial detail, preservation of unaffected content, motion and continuity across repeated attempts.
CapabilitiesBreadth and reliability of demonstrated useful production functions, not announced feature counts.

Quality and editing categories keep price and access separate from the order. Best Overall includes practical workflow, access and value after quality. Capabilities assesses demonstrated usefulness and reliability, not advertised feature counts. Multiple versions from one family can place when their exact configurations earn it.

Video quality uses a consistent comparison.

For text-to-video, we consider visual realization, believable motion and physics, temporal coherence, scene composition and meaningful direction together. Literal camera adherence is important, but does not automatically outweigh the quality of the finished scene. Missing subjects, merged bodies and other artifacts remain genuine failures. Audio-inclusive preference and price do not decide visual quality.

Each adjacent placement explains the scene and attempt coverage and the tested configurations. A handful of 720p generations cannot establish performance across a model’s best available settings. We preserve those limits and never assume a higher-resolution option fixes observed failures.

Optional owner editorial judgment.

Trevor Mayes may contribute a dated filmmaking assessment with his preferred winner, reasoning and viewing basis. When this influences a published order, we identify it publicly and preserve contrary external evidence. It is first-party editorial judgment, not an independent benchmark, blind BMN test or hidden numerical bonus. A decisive owner preference carries limited confidence. Future research must explicitly address it and may reach a different conclusion with stated reasons; it is not a permanent model-specific rule. Trevor can revise or withdraw it for future reviews.

Image-to-video means rendered visual quality.

We compare motion and physics, source identity and composition, temporal stability, detail and artifacts, and instruction fidelity. Single-image animation and multi-reference filmmaking are distinct tests. Price, generation speed, access and audio preference do not decide this quality ranking.

A narrow Elo lead in an audio-inclusive or mixed-resolution pool cannot establish a visual-quality win. We examine original hands-on comparisons alongside those benchmarks, preserve contrary findings, and state differences in resolution, host, input mode and retries. Unmeasured dimensions stay unmeasured. An unresolved comparison can support a limited-confidence editorial choice, not a claim of demonstrated superiority. The same requirements apply to every model.

Photorealism includes ordinary photographs.

A believable phone snapshot can be as successful as a polished studio portrait. We judge the kind of photograph requested: everyday phone snapshots; low-light, flash and motion on phones; professional portraits and editorial; product and commercial photography; documentary, street and events; landscape and architecture. Appropriate noise, flash, softness or imperfect framing is not an AI defect. Unrequested glamour lighting and glossy skin can make a casual photograph less convincing. Impossible anatomy, objects and reflections remain defects.

Each Photorealism review records coverage and gaps across all six photo types. Repeated evidence about professional portraits cannot establish casual-phone realism. Incomplete coverage lowers confidence in the overall order; it does not require an unordered list.

Video editing must preserve what you meant to keep.

We check the requested change, natural skin and faces, unchanged details, motion and consistency across attempts. Tests of newly generated scenes do not prove editing quality. An audio-inclusive vote lead does not establish better visual detail; missing comparisons remain explicit.

Confidence belongs beside the recommendation.

Limited, moderate and strong describe confidence in our editorial ordering, not a calibrated probability. A missing benchmark entry is not a loss. We retain contrary findings, exact versions, resolutions, input modes and original source dates. Two independent comparative publishers must support the selected field, and every selected configuration needs relevant direct comparative evidence. Strong confidence additionally requires two independent direct publishers supporting the winner; incomplete photo-type coverage prevents strong confidence in Photorealism.

An unresolved contender must stay visible.

Eligible contenders awaiting comparative evidence are named beside the recommendations across images, video, technical coding, app builders, General Writing and Business Writing. Being included in the field is different from having a completed comparison. Missing a leaderboard entry does not exclude a model, and it does not establish that the published choices are better. Fiction Prose and Screenwriting retain their separate blind-test and owner-review process.

We track how long each comparison has remained unresolved. After seven days it becomes priority research, even if the category or its sources were checked recently. The next step must seek the missing task-specific evidence through original hands-on comparisons or accessible original articles, transcripts and outputs. Repeating an availability check does not restart that clock. Different settings can inform a clearly limited editorial judgment when their effects are disclosed; we do not require every model to appear in one perfectly identical benchmark.

Escalation does not automatically promote a model or authorize paid testing. Source independence, exact-version relevance and publication safeguards still apply. Until evidence supports a new order, the existing recommendation remains provisional and the unresolved contenders stay visible.

A category review can also publish useful initial findings about a contender without assigning a new rank or numerical score. These findings state task-specific strengths, limitations and the next comparison needed. General Writing and Business Writing can publish supported provisional rankings independently of the creative-writing approval process; they still require evidence for the complete eligible field and the five scoring factors. Technical coding retains its exact-configuration benchmark and corroboration requirements, and builders retain their product-demonstration and consensus requirements.

Hands-on comparisons with affiliate, sponsored, free-access or undisclosed relationships may supplement the review with their limits stated, but never meet the independence minimum. Historical unordered reviews remain available for rollback; a new numbered recommendation requires a documented reassessment. Rereading a test does not make it new.

A score alone is not enough.

  • The defined, dated eligible field must be completely reviewed. Qualified media recommendations record a disposition for each contender; weighted rankings require complete scorecards. This means the registered field for that evaluation—not every model in existence. New discoveries pass an eligibility review before joining it.
  • Contenders must be named at the level actually tested: model, system, platform, or configuration.
  • Evidence must meet the category's relevance, independence, freshness, and diversity threshold.
  • Exact weighted ties break on category performance, then real-world preference, access and workflow, restrictions, and price and value.
  • A fourth-place contender within one weighted point of third appears as Also excellent.
  • Builder rankings remain provisional until Practical Consensus is complete.
  • Fiction Prose and Screenwriting challenger updates require both rounds of the defined top-three-plus-challengers field to be completed and human reviewed, followed by owner confirmation.
  • Qualifying changes publish automatically with an audit trail and a restorable prior version.
  • Dates, versions, access plans, routing, and material limitations must be recorded.

Rankings cannot be purchased. A commercial relationship with Best Model Now never changes eligibility, scoring, or placement; relationships disclosed by outside evidence sources are discounted under the policy above. Material corrections are recorded and rankings are reverified as products change.