{"slug": "kimi-k3-1-scores-55-on-artificial-analysis-on-the-pareto-line", "title": "Kimi K3.1 scores 55 on Artificial Analysis, on the Pareto line", "summary": "Kimi K3.1 Pro scored 55 on the Artificial Analysis Intelligence Index, well above the median of 27 for comparable models, according to Artificial Analysis's model analysis. The reasoning model, which accepts text and image input and has a 1M-token context window, is priced at $2.00 per 1M input tokens and $10.00 per 1M output tokens, costing $2.11 per task to evaluate, and it generated 150M tokens during the Intelligence Index run versus a median of 87M. At 73 tokens per second, Kimi K3.1 Pro runs slower than the average of 81 tokens per second.", "body_md": "# Kimi K3.1 Pro Intelligence, Performance & Price Analysis\n\n### Model summary\n\nKimi K3.1 Pro is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. It's also slower than average and very verbose. The model supports text and image input, outputs text, and has a 1M tokens context window.\n\nKimi K3.1 Pro scores 55 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 27). When evaluating the Intelligence Index, it generated 150M tokens, which is very verbose in comparison to the median of 87M.\n\nPricing for Kimi K3.1 Pro is $2.00 per 1M input tokens (moderately priced, median: $2.00) and $10.00 per 1M output tokens (moderately priced, median: $10.00). On average, it costs $2.11 per task to evaluate Kimi K3.1 Pro on the Intelligence Index.\n\nAt 73 tokens per second, Kimi K3.1 Pro is slower than average (81).\n\n| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. | \n|---|---|\n| Input modality | Supports: text and image | \n| Output modality | Supports: text | \n| Context window | 1M ~1500 A4 pages of size 12 Arial font | \n\nMetrics are compared against models of the same class:\n\n- Non-reasoning models → compared only with other non-reasoning models\n- Reasoning models → compared across both reasoning and non-reasoning\n- Open weights models → compared only with other open weights models of the same size class:\n- Tiny: ≤4B parameters\n- Small: 4B–40B parameters\n- Medium: 40B–150B parameters\n- Large: >150B parameters\n- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:\n- <$0.15 per 1M tokens\n- $0.15–$1 per 1M tokens\n- >$1 per 1M tokens\n\n## Intelligence\n\n### [Artificial Analysis Intelligence Index](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index)\n\n### Artificial Analysis Intelligence Index by Open Weights / Proprietary\n\n### Intelligence Index vs. Cost per Task\n\nMeasures the performance of models on specific capabilities and industries\n\n### [Artificial Analysis Finance & Accounting Index](https://artificialanalysis.ai/indexes/finance-and-accounting)\n\n### Evaluations\n\nAgentic knowledge work, Elo\n\nKnowledge\n\n1 - hallucination rate\n\nAgentic SaaS workflows\n\nAgentic real-world work tasks, Elo\n\nReasoning & knowledge\n\nProfessional document reasoning, All-pass\n\nAgentic coding & terminal use\n\nLong context reasoning\n\n[SciCode](https://artificialanalysis.ai/evaluations/scicode)Under review\n\nCoding\n\nPhysics reasoning\n\nAgentic scientific research workflows in a terminal\n\nAgentic software engineering in open-source repositories\n\nQuantitative analysis on spreadsheets & documents\n\n[Harvey LAB-AA v1.1](https://artificialanalysis.ai/evaluations/harvey-lab-aa)Updated\n\nLegal agentic work, Hallucination-Gated All-Pass Rate\n\nAgentic business operations\n\n[AA-3D](https://artificialanalysis.ai/evaluations/aa-3d)New\n\nAgentic interactive 3D experiences, Elo\n\nKubernetes incident root-cause analysis\n\n[MLCR-AA](https://artificialanalysis.ai/evaluations/mlcr-aa)Updated\n\nMedical long context reasoning\n\nVisual reasoning\n\nRich, varied, well-crafted language\n\nWell-structured, scannable, answer-first\n\nTruthful, acknowledges uncertainty, non-sycophantic\n\nUse of filler and generic language\n\n### AA-Briefcase v1.1\n\n### AA-Briefcase Elo\n\n### AA-Omniscience\n\n### AA-Omniscience Index\n\n## Intelligence Index Comparisons\n\n### Intelligence Index vs. Cost per Task\n\n## Token Use\n\n### Output Tokens per Intelligence Index Task\n\n## Cost\n\n### Cost per Intelligence Index Task\n\n### Cost to Run Artificial Analysis Intelligence Index\n\n### Pricing: Cache Hit, Input, and Output\n\n## Context Window\n\n### Context Window\n\n## Speed\n\nMeasured by Output Speed (tokens per second)\n\n### Output Speed\n\n### Time per Intelligence Index Task\n\n## Latency\n\nMeasured by Time (seconds) to First Token\n\n### Latency: Time To First Answer Token\n\n## End-to-End Response Time\n\nSeconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed\n\n### End-to-End Response Time\n\n## Frequently Asked Questions\n\nCommon questions about Kimi K3.1 Pro\n\nKimi K3.1 Pro was released on October 23, 2026.\n\nKimi K3.1 Pro was created by Kimi.\n\nKimi K3.1 Pro scores 55 on the Artificial Analysis Intelligence Index, placing it well above average among other models in a similar price tier (median: 27).\n\nKimi K3.1 Pro generates output at 73.5 tokens per second (based on Kimi's API), which is below average compared to other models in a similar price tier (median: 81.1 t/s).\n\nKimi K3.1 Pro has a time to first token (TTFT) of 3.20s (based on Kimi's API), which is better than average compared to other models in a similar price tier (median: 3.62s).\n\nKimi K3.1 Pro costs $2.00 per 1M input tokens (better than average, median: $2.00) and $10.00 per 1M output tokens (better than average, median: $10.00), based on Kimi's API.\n\nKimi K3.1 Pro costs $2.00 per 1M input tokens and $10.00 per 1M output tokens (based on Kimi's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $1.54 per 1M tokens. Pricing may vary by provider. [Compare provider pricing](https://artificialanalysis.ai/models/kimi-k3-1-pro/providers)\n\nWhen evaluated on the Intelligence Index, Kimi K3.1 Pro generated 150M output tokens, which is at the higher end compared to other models in a similar price tier (median: 87M).\n\nYes, Kimi K3.1 Pro is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.\n\nKimi K3.1 Pro supports text and image input.\n\nKimi K3.1 Pro supports text output.\n\nYes, Kimi K3.1 Pro supports image input and can analyze, describe, and answer questions about images.\n\nYes, Kimi K3.1 Pro is multimodal. It can process text and image input and generate text output.\n\nKimi K3.1 Pro has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.\n\nNo, Kimi K3.1 Pro is proprietary. The model weights are not publicly available.\n\nKimi K3.1 Pro has 2.8 trillion parameters.\n\nKimi K3.1 Pro achieves a score of 55 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.\n\nYes, Kimi K3.1 Pro is available via API through 1 provider. [Compare API providers](https://artificialanalysis.ai/models/kimi-k3-1-pro/providers)\n\nKimi K3.1 Pro is available through 1 API provider. [Compare providers](https://artificialanalysis.ai/models/kimi-k3-1-pro/providers)", "url": "https://wpnews.pro/news/kimi-k3-1-scores-55-on-artificial-analysis-on-the-pareto-line", "canonical_source": "https://artificialanalysis.ai/models/kimi-k3-1-pro", "published_at": "2026-10-23 18:24:54+00:00", "updated_at": "2026-10-23 18:42:56.442320+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": ["Kimi K3.1 Pro", "Artificial Analysis", "Artificial Analysis Intelligence Index"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/kimi-k3-1-scores-55-on-artificial-analysis-on-the-pareto-line", "markdown": "https://wpnews.pro/news/kimi-k3-1-scores-55-on-artificial-analysis-on-the-pareto-line.md", "text": "https://wpnews.pro/news/kimi-k3-1-scores-55-on-artificial-analysis-on-the-pareto-line.txt", "jsonld": "https://wpnews.pro/news/kimi-k3-1-scores-55-on-artificial-analysis-on-the-pareto-line.jsonld"}}