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AI · LEADERBOARD

最強ではなく、仕事に合うモデルを選ぶ。

今導入できるAIモデルを、知能、速度、コスト、コンテキスト、運用条件で比較します。

Snapshot: 16 models compared

Independent editorial snapshot · not live platform telemetry

Dated editorial snapshot

Review target: weekly and after major releases. Rankings reflect this dated snapshot. OpenRouter usage below has its own source timestamp.

The short answer

Three leaders, three different decisions.

Best overall

Closed model

Anthropic

Intelligence
63.1
Speed
58.8tok/s
Input / output
$5 / $25
Context
1M

Best fit

State-of-the-art reasoning and complex agentic workflows

Best open model

Open weights

Kimi

Intelligence
59.7
Speed
35tok/s
Input / output
$3 / $15
Context
1.05M

Best fit

Sovereign, open-weight deployments needing maximum intelligence

Best value

Open weights

DeepSeek

Intelligence
51.8
Speed
115tok/s
Input / output
$0.14 / $0.28
Context
1.31M

Best fit

Cost-sensitive open inference and coding workloads

OpenRouter usage

OpenRouter LLM usage ranking

Top models by prompt, completion, and reasoning tokens processed on OpenRouter. Showing the first 20 only.

Top 20OpenRouter data · cached hourlyUpstream · Oct 6, 2026
  1. 1.DeepSeek V4.1 Flash (batch)by DeepSeek29.6T tokens34%
  2. 2.GLM 5.3 Flash (batch)by Z.ai10.2T tokens12%
  3. 3.MiMo-V2.6-Flashby Xiaomi9.87T tokens20%
  4. 4.GPT-6 Luna (batch)by OpenAI6.44T tokens47%
  5. 5.Hy4 previewby Tencent6.42T tokens20%
  6. 6.DeepSeek V4 Flash 0731by DeepSeek5.79T tokens20%
  7. 7.Nemotron 3 Ultra (free)by Nvidia5.68T tokens3%
  8. 8.DeepSeek V4 Flash 0423by DeepSeek3.23T tokens1%
  9. 9.GLM 5.3 (batch)by Z.ai3.06T tokens9%
  10. 10.Claude Opus 5.5 (batch)by Anthropic2.93T tokens73%
  11. 11.GPT-5.6 Luna (batch)by OpenAI2.36T tokens72%
  12. 12.Gemini 3.8 Flash (batch)by Google2.17T tokens6%
  13. 13.GPT-5.6 Sol (batch)by OpenAI1.77T tokens8%
  14. 14.Kimi K3 (batch)by Moonshot AI1.7T tokens14%
  15. 15.Hy3by Tencent1.68T tokens31%
  16. 16.Muse Spark 1.3 Contributorby Meta1.43T tokens7%
  17. 17.GLM 5.2by Z.ai1.42T tokens3%
  18. 18.GPT-6 Astra (batch)by OpenAI1.38T tokens50%
  19. 19.MiMo-V2.6-Proby Xiaomi1.22T tokens1%
  20. 20.Claude Sonnet 5.5 (batch)by Anthropic1.19T tokens955%

From benchmark to decision

Which model for which job?

Nine recurring production scenarios, with the trade-offs that matter before a team commits to a model.

Selected use case

01 / 09

Decision view

Customer support

Why it fits

  1. 01High measured throughput
  2. 02Low per-token cost
  3. 03Strong intelligence for support

How the ranking works

Transparent enough to disagree with.

The ranking combines public benchmark signals with price, measured throughput, context and deployment constraints. It is a decision aid, not a universal definition of model quality.

Dataset snapshot
25 Aug 2026 · 10:41 TST
Review cadence
Revalidated weekly and after major releases

Value score

(intelligence − 25)² × min(1, speed ÷ 30) ÷ (input cost + output cost × 3)

Output is weighted ×3 to reflect output-heavy production use; intelligence is squared so cheap but weak models do not dominate.

Intelligence
Public benchmark and reasoning signals form the default order.
Speed
Output tokens per second; provider and region still affect real latency.
Cost
Public input and output price per million tokens, before enterprise discounts.
Deployment
Context, weight access and hosting constraints determine production fit.

Public references

Prices and performance can change without notice. Validate the finalists against your own prompts, infrastructure and risk requirements before procurement.

FAQ

Questions worth asking before the benchmark wins the argument.

TENTEN AI · MODEL EVALUATION

The leaderboard is the start

Bring the workflow. We’ll test the shortlist.

A model only becomes a good decision after it survives your documents, permissions, latency budget and failure cases.