Author's Description
An instruction-tuned, hybrid-reasoning Mixture-of-Experts model built on Llama-4-Scout-17B-16E. Cogito v2 can answer directly or engage an extended “thinking” phase, with alignment guided by Iterated Distillation & Amplification (IDA). It targets coding, STEM, instruction following, and general helpfulness, with stronger multilingual, tool-calling, and reasoning performance than size-equivalent baselines. The model supports long-context use (up to 10M tokens) and standard Transformers workflows. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)
Key Specifications
Supported Parameters
This model supports the following parameters:
Features
This model supports the following features:
Performance Summary
Cogito V2 Preview Llama 109B demonstrates exceptional speed, consistently ranking among the fastest models across all evaluated benchmarks. Its pricing strategy generally offers cost-effective solutions, placing it in the 66th percentile for affordability. While specific reliability metrics are not provided, the model's performance across benchmarks suggests a functional operational state. A significant strength is its perfect accuracy in the Hallucinations (Baseline) test, indicating a robust ability to acknowledge uncertainty and avoid generating fictional information. This makes it a highly reliable choice for applications where factual integrity is paramount. The model also shows promising performance in Reasoning, achieving 64.0% accuracy, which is competitive within its price point and speed category. However, the model exhibits critical weaknesses in several core areas. It scored 0.0% accuracy across Instruction Following, General Knowledge, Coding, Email Classification, and Ethics benchmarks. This suggests a substantial limitation in understanding and executing complex instructions, recalling factual information, performing coding tasks, categorizing emails, and navigating ethical dilemmas. These widespread zero-accuracy scores indicate that while the model can identify when it doesn't know something, it struggles significantly with tasks requiring specific knowledge, complex instruction adherence, or domain-specific understanding.
Model Pricing
Current Pricing
Feature | Price (per 1M tokens) |
---|---|
Prompt | $0.18 |
Completion | $0.59 |
Price History
Available Endpoints
Provider | Endpoint Name | Context Length | Pricing (Input) | Pricing (Output) |
---|---|---|---|---|
Together
|
Together | deepcogito/cogito-v2-preview-llama-109b-moe | 32K | $0.18 / 1M tokens | $0.59 / 1M tokens |
Benchmark Results
Benchmark | Category | Reasoning | Strategy | Free | Executions | Accuracy | Cost | Duration |
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Other Models by deepcogito
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Released | Params | Context |
|
Speed | Ability | Cost |
---|---|---|---|---|---|---|---|
Deep Cogito: Cogito V2 Preview Llama 405B Unavailable | Oct 17, 2025 | 405B | 32K |
Text input
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Deep Cogito: Cogito V2 Preview Llama 70B | Sep 02, 2025 | 70B | 32K |
Text input
Text output
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— | — | — |
Deep Cogito: Cogito V2 Preview Deepseek 671B | Sep 02, 2025 | 671B | 163K |
Text input
Text output
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★★★ | ★ | $$$$ |