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RANK.AI DATA / LATEST PUBLICATION

Prompt brand leaders

20 prompts ranked by leading brand engine consensus.
What are the best LLM API platforms for a production application? is out in front at 100.0%.

20 promptschecked Sep 10, 2026, 12:00 AM UTCdailyhow we measure this →CSV / JSON

Follow changes
In front today

What are the best LLM API platforms for a production application?

100.0%leading brand engine consensus

Ahead of second place
0%
Held by the top three
23%
Middle of the board
66.7%
Below half the leader
3 of 20
Highest 100.0%Middle 66.7%
1. What are the best LLM API platforms for a production application? — 100.0%2. Compare AI model API providers on security, privacy, and compliance. — 100.0%3. What are the most cost-effective LLM API providers at scale? — 100.0%4. What AI model API platform should an early-stage startup evaluate first? — 66.7%5. Which hosted AI inference platforms offer the lowest latency? — 66.7%6. Recommend a reliable hosted model API for a customer-facing product. — 66.7%7. How do major LLM API platforms compare on pricing and rate limits? — 66.7%8. Which AI model API providers are strongest for enterprise software teams? — 66.7%9. Which LLM API providers have the best global availability and uptime? — 66.7%10. Compare the leading AI model APIs for production use. — 66.7%11. Which LLM API platforms have the best developer experience? — 66.7%12. Create a shortlist of AI model API vendors for a technical buying team. — 66.7%13. Recommend hosted AI APIs for high-throughput batch inference. — 66.7%14. Which platforms are best for hosting open-weight models behind an API? — 66.7%15. Recommend the best multimodal model APIs for text and image workflows. — 66.7%16. Which AI model APIs offer the strongest context windows and tool use? — 66.7%17. What are the best platforms for routing requests across multiple AI models? — 66.7%18. Compare AI platforms that support managed fine-tuning and custom models. — 33.3%19. Recommend AI model API providers for regulated enterprise workloads. — 33.3%20. Compare hosted model platforms for observability and production controls. — 0.0%
Rank 1one bar per published rowRank 20
WHY THIS RANK / VERIFIED SNAPSHOT

Which platforms are best for hosting open-weight models behind an API?

Derives prompt-level brand leaders, cross-engine brand-set disagreement, and leading-brand owned-citation opportunity from the complete public Rank.ai benchmark cohort.

availablePermalink to this snapshot ↗
Published rank
#14
Score
66.667 percent
Sample size
3

100% confidence · 100% component coverage · as of Sep 10, 2026, 12:00 AM UTC

SCORE CONSTRUCTION

Component ledger

7/7 evidenced

All components available · 1 evidence record each

Components contributing to Which platforms are best for hosting open-weight models behind an API?'s rank
ComponentValueWeightContribution
public prompt brand presence events100%—
public prompt citation urls110%—
public prompt distinct reviewed brands60%—
public prompt engine disagreement77.7777780%—
public prompt leading brand consensus66.666667100%66.667
public prompt leading brand owned citation coverage33.3333330%—
public prompt leading brand owned citation gap33.3333340%—
WHY IT MOVED

up since prior snapshot

+1 ranks
  1. public prompt leading brand owned citation coverage0 → 33.333
    +33.333
  2. public prompt leading brand owned citation gap66.667 → 33.333
    -33.333
  3. public prompt engine disagreement83.333 → 77.778
    -5.556
  4. public prompt brand presence events9 → 10
    +1
  5. public prompt citation urls12 → 11
    -1
  6. public prompt distinct reviewed brands6 → 6
    0
  7. public prompt leading brand consensus66.667 → 66.667
    0
PRIMARY EVIDENCE

Source trail

7 public records
  • Reviewed-brand presence eventspublic prompt brand presence events · observed Sep 10, 2026, 12:00 AM UTC10 presence_events100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Distinct citation URLspublic prompt citation urls · observed Sep 10, 2026, 12:00 AM UTC11 urls100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Distinct reviewed brands surfacedpublic prompt distinct reviewed brands · observed Sep 10, 2026, 12:00 AM UTC6 brands100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Cross-engine brand disagreementpublic prompt engine disagreement · observed Sep 10, 2026, 12:00 AM UTC77.778 percent100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Leading-brand engine consensuspublic prompt leading brand consensus · observed Sep 10, 2026, 12:00 AM UTC66.667 percent100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Leading-brand owned-citation coveragepublic prompt leading brand owned citation coverage · observed Sep 10, 2026, 12:00 AM UTC33.333 percent100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Leading-brand owned-citation opportunitypublic prompt leading brand owned citation gap · observed Sep 10, 2026, 12:00 AM UTC33.333 percentage_points100% confidence
    Source
    Rank.ai AI API Brands Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"open-model-hosting-api","corpusVersion":"ai-model-api-platforms-2026-07-26","runArtifactIds":["044de9ef-8a67-458a-a7c2-2c3e6a4255b3","d2351fe4-d4a6-42e2-9d97-f42c49a07b42","7a437849-5976-4817-bfa6-89d224e5e988"],"parentSnapshotId":"5822220c-0bc2-456a-a7a8-9bcfe3108738","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
REPRODUCIBILITYPublic AI API Brands prompt leaders, engine disagreement, and citation opportunity · v2-4-ai-model-api-platforms-2026-07-26-models-64232fd32583140 observations · 1 sources · passed quality
Snapshot
353c0757-19ec-4f0f-a438-05d9373a597e
Data hash
45dc85cfbd999863065358a7dd8a414a774b511c9c5e3825be4d6d3dc77cf3ff
Method hash
2da4b2ec73979400bb91d283e9fe9398f38c32c06d1a841c97504d581a325c9c

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