Specifications
Context Window
—
Input Price/1M
$1.25
Output Price/1M
$1.25
Parameters
—
Speed
86 tok/s
Latency (TTFT)
474ms
Benchmarks
Cogito v2.1 (Reasoning) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Agentic
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 17.0 | 100.0 | — |
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 69.0 | 100.0 | Artificial Analysis official API |
| SciCode | 41.0 | 100.0 | — |
| AA Coding Index | 24.8 | 100.0 | Artificial Analysis official API |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 85.0 | 100.0 | — |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 22.0 | 100.0 | — |
Math
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AIME 2025 | 73.0 | 100.0 | Artificial Analysis official API |
| AA Math Index | 72.7 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 84.9 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 77.0 | 100.0 | Artificial Analysis official API |
| IFBench | 46.0 | 100.0 | — |
| HLE | 11.0 | 100.0 | — |
Information
- Release date
- November 18, 2025
- Tool Calling
- ❌ Not supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: Cogito v2.1 (Reasoning)
What is Cogito v2.1 (Reasoning)?
Cogito v2.1 (Reasoning) is an AI model developed by Deep Cogito, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Deep Cogito's cloud API.
Pricing & Costs in 2026
Cogito v2.1 (Reasoning) is usage-based, priced at $1.25/1M input tokens and $1.25/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The mid-range pricing balances quality and cost for most professional applications.
Benchmarks & Performance
Cogito v2.1 (Reasoning) was evaluated on 12 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, Reasoning. Results show solid performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
Cogito v2.1 (Reasoning) specializes in text, offering advanced capabilities for creating and processing text content.
Comparison with Alternatives
In the 2026 AI model ecosystem, Cogito v2.1 (Reasoning) competes directly with similarly capable models. Deep Cogito competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is Cogito v2.1 (Reasoning)?
Cogito v2.1 (Reasoning) is an AI model developed by Deep Cogito. It is a text model.
How much does Cogito v2.1 (Reasoning) cost?
Cogito v2.1 (Reasoning) costs $1.25/1M input tokens and $1.25/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does Cogito v2.1 (Reasoning) compare with other models?
In available benchmarks, Cogito v2.1 (Reasoning) scored: Terminal-Bench Hard: 17/100, LiveCodeBench: 69/100, SciCode: 41/100. See the full table above for a detailed comparison.
Is Cogito v2.1 (Reasoning) open source?
No, Cogito v2.1 (Reasoning) is a proprietary model from Deep Cogito. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is Cogito v2.1 (Reasoning) best for?
Cogito v2.1 (Reasoning) excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →