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Cogito v2.1 671B
- Text Generation
Cogito v2.1 671B is Deep Cogito’s flagship 671B-parameter open-weight Mixture-of-Experts language model optimized for efficient hybrid reasoning. It delivers frontier-level performance while using significantly shorter reasoning chains than comparable models.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Cogito v2.1 671B?
Cogito v2.1 671B is a large 671B-parameter Mixture-of-Experts hybrid reasoning language model released by Deep Cogito under an open license for commercial use. It is mainly used for advanced instruction following, coding and STEM tasks, and handling long, multi-turn text generation with a 128K-token context window. The model is also applied to creative writing, tool calling, and other complex reasoning workloads where it rivals frontier closed and open models while using fewer reasoning tokens. It belongs to the Cogito model family and is a v2.1-generation successor building on earlier Cogito hybrid reasoning LLMs.
Model capabilities
5 Core Capabilities
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Conversational AI
Engages in multi-turn, context-aware dialogue, answering questions and following instructions across many domains with coherent, detailed responses.
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Visual Reasoning
Interprets images to identify objects, relationships, and scenes, supporting tasks like description, comparison, and simple visual reasoning.
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Text Translation
Translates text between multiple major languages, preserving meaning and tone while adapting to context and domain-specific terminology.
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Text Extraction
Extracts structured text and key information from documents or screenshots, enabling downstream search, analysis, and content transformation.
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Content Moderation
Monitors and classifies user-generated content for safety, policy violations, and sensitive topics to support compliant application experiences.
Use cases
6 Most Valuable Use Cases
- Advanced Code Generation
- STEM Problem Solving
- Complex Legal Research
- Financial Report Analysis
- Enterprise Knowledge RAG
- Automated Case Monitoring
Transparent pricing
Cost Comparison
LLM API offers the lowest costs and fastest performance for Cogito v2.1‑class 671B models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.60 | $1.80 | 256K tokens |
| Deep Cogito | US East | ~160ms | ~40 tps | ~99.9% | ~$0.90 | ~$2.70 | ~128K tokens |
| AWS Bedrock (3rd‑party host) | US West | ~190ms | ~30 tps | ~99.9% | ~$1.10 | ~$3.30 | ~128K tokens |
| Azure AI Model Hosting | EU West | ~200ms | ~28 tps | ~99.9% | ~$1.20 | ~$3.60 | ~128K tokens |
| GCP Vertex AI Extensions | Global | ~210ms | ~25 tps | ~99.9% | ~$1.30 | ~$3.90 | ~128K tokens |
Performance benchmarks
Technical Specifications
| Metric | Cogito v2.1 671B (Deep Cogito) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~220ms | ~300ms | ~280ms |
| Context Window | 200K | 128K | 200K |
| Input Price ($/1M tokens) | $2.20 | $5.00 | $3.00 |
| Output Price ($/1M tokens) | $6.50 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 60 tps | 40 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 112.5B
- Prompt tokens processed (30 days)
- 86.3B
- Completion tokens generated (30 days)
- 9.4M
- API requests served (30 days)
- 98.7%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers based on latency, quality, or custom rules—no client changes, just smarter traffic.
One endpoint, every model. -
Cost-Aware Orchestration
Automatically balance premium and budget models using your policies, so you cut spend without sacrificing SLAs, accuracy, or end-user experience.
Optimize spend by design. -
Resilient Fallback Flows
Define multi-step failover chains so if a provider degrades or times out, requests seamlessly retry on backups—no downtime, no manual rewiring.
Failure-safe by default. -
Full-Stack Observability
Get unified logs, traces, metrics, and per-model analytics across all providers to debug faster, tune prompts, and prove reliability to stakeholders.
See every token, everywhere. -
Task-Level Abstractions
Call high-level tasks like “chat”, “extract”, or “moderate” instead of provider-specific APIs, so you can swap models without refactoring application code.
Code to tasks, not vendors. -
High-Throughput Batch Jobs
Run large-scale batch inferences with automatic chunking, retries, and rate control, turning millions of records into a single, predictable job.
Batch at production scale.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need very strong general-purpose reasoning from a frontier-scale, cutting-edge large model.
- You need high-quality, nuanced natural language understanding and generation across many domains.
- Your use case involves complex multi-step problem solving, planning, or tool-using agent workflows.
- Your use case involves drafting and refining long-form content that benefits from rich context.
- You need a single, powerful model to prototype varied applications before later specialization.
- You need robust handling of ambiguous instructions where safer, conservative behavior is preferable.
Avoid if...
- You need extremely low-cost inference for simple classification or keyword extraction tasks.
- Your workload requires strict real-time latency guarantees on low-end or edge hardware.
- You need on-device deployment where memory and compute budgets are very constrained.
- Your workload requires vision, audio, or multimodal understanding not supported by this model.
- You need a fully open-weight model that can be self-hosted without external dependency.
- Your workload requires fine-tuning or domain adaptation capabilities this hosted model does not expose.
FAQ
Frequently Asked Questions
-
What is Cogito v2.1 671B?
Cogito v2.1 671B is a large‑scale language model by Deep Cogito focused on high‑quality reasoning, coding, and complex instruction following.
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What is Cogito v2.1 671B best suited for?
It is best for multi-step reasoning, complex code generation, data analysis assistance, and building sophisticated chat or agent-style applications.
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How is Cogito v2.1 671B priced on LLM.API?
Pricing for Cogito v2.1 671B is set by LLM.API; check your LLM.API dashboard or pricing page for current per-token rates.
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What context window does Cogito v2.1 671B support?
Cogito v2.1 671B supports a context window size that is defined by LLM.API; see the model details in the LLM.API documentation.
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How fast is Cogito v2.1 671B when called through LLM.API?
Latency depends on your region, request size, and LLM.API load, but 671B-parameter models are generally slower than smaller alternatives.
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What input and output modalities does Cogito v2.1 671B support on LLM.API?
On LLM.API, Cogito v2.1 671B currently supports text input and text output; other modalities depend on future LLM.API feature support.
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How do I call Cogito v2.1 671B via the LLM.API gateway?
Use the standard LLM.API completion or chat endpoint with the model parameter set to the Cogito v2.1 671B identifier in your account.
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How does Cogito v2.1 671B compare to similar large models?
It prioritizes strong logical reasoning and code reliability, trading slightly higher latency and cost compared to smaller, speed-optimized models.
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What are the main limitations of Cogito v2.1 671B?
It can hallucinate, reflect training-data biases, incur higher costs on long contexts, and should not be used without human oversight for critical decisions.
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Can I fine-tune Cogito v2.1 671B via LLM.API?
Direct fine-tuning may not be available; instead, use system prompts, retrieval-augmented generation, and LLM.API configuration to specialize behavior.
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