Best overall
Closed model
Anthropic
- Intelligence
- 60.7
- Speed
- 61tok/s
- Input / output
- $5 / $25
- Context
- 1M
Best fit
State-of-the-art reasoning and complex agentic workflows
AI · LEADERBOARD
Choose the model that fits the work.
A practical ranking of the AI models teams can deploy now—compared by intelligence, speed, cost, context and operational fit.
Snapshot: 5 Aug 2026 · 13:01 TST12 models compared
Independent editorial snapshot · not live platform telemetry
The short answer
Three leaders, three different decisions.
Best open model
Open weights
Kimi
- Intelligence
- 57.1
- Speed
- 36tok/s
- Input / output
- $3 / $15
- Context
- 1.05M
Best fit
Sovereign, open-weight deployments needing maximum intelligence
Best value
Open weights
Xiaomi
- Intelligence
- 37.2
- Speed
- 82tok/s
- Input / output
- $0.14 / $0.28
- Context
- 1.05M
Best fit
Cost-sensitive extraction and high-volume structured work
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 · Aug 7, 2026
- 1.DeepSeek V4 Flash 0731by DeepSeek7.46T tokens>999%
- 2.Hy3by Tencent6.24T tokens30%
- 3.DeepSeek V4 Flash 0423by DeepSeek6.15T tokens19%
- 4.MiMo-V2.5by Xiaomi5.31T tokens34%
- 5.GPT-5.6 Luna (batch)by OpenAI4.45T tokens432%
- 6.GLM 5.2 (batch)by Z.ai3.18T tokens4%
- 7.DeepSeek V4 Proby DeepSeek2.51T tokens30%
- 8.Nemotron 3 Ultra (free)by Nvidia2.39T tokens7%
- 9.Gemini 3.6 Flash (batch)by Google2.33T tokens479%
- 10.Laguna S 2.1 (free)by Poolside1.85T tokens242%
- 11.MiniMax M3 (batch)by MiniMax1.72T tokens15%
- 12.Kimi K3by Moonshot AI1.37T tokens1%
- 13.Step 3.7 Flashby StepFun1.3T tokens24%
- 14.Claude Opus 5 (batch)by Anthropic1.16T tokens26%
- 15.Claude Sonnet 5 (batch)by Anthropic1.05T tokens3%
- 16.Ling-3.0-flash (free)by Inclusion AI954B tokens29%
- 17.Gemini 3 Flash Preview (batch)by Google912B tokens7%
- 18.Claude Sonnet 4.6 (batch)by Anthropic827B tokens15%
- 19.GPT-5.6 Terra (batch)by OpenAI776B tokens104%
- 20.Gemini 2.5 Flash Lite (batch)by Google611B tokens9%
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
- 01288 tok/s keeps wait time low
- 02Multilingual intake
- 03Strong multimodal routing
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
- 5 Aug 2026 · 13:01 TST
- Review cadence
- Revalidated when major model data changes
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.
Claude Opus 5 leads the intelligence-first ranking in this captured dataset. The best choice can change once price, latency, hosting and task-specific evaluation enter the decision.
Kimi K3 is the leading open-weight option in this snapshot, combining the strongest open-model intelligence signal with a 1M-token context window and sovereign deployment flexibility.
MiMo-V2.5 leads the value formula because its low token price and large context offset a lower raw intelligence score. It is particularly compelling for structured, high-volume work.
No. Context capacity only describes how much can be submitted. Retrieval design, instruction quality, attention behaviour and evaluation still determine whether that context produces a useful answer.
This page is an editorial snapshot, not live platform telemetry. We revalidate the dataset when major model releases or material pricing and performance changes occur.
The default order uses the public intelligence signal. Speed, price, context, openness and a separate value formula remain visible so teams can challenge the default order.
Tenten does not sell model tokens through this page. Public sources are linked, assumptions are shown, and we recommend a task-specific evaluation before any production decision.
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.