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INTELLECT-3

  • Instruction Following

INTELLECT-3 is an AI model from Prime Intellect, but publicly available technical details about its architecture, capabilities, and benchmarks are not documented. Information about its specific strengths or distinguishing features is currently unavailable.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is INTELLECT-3?

INTELLECT-3 is an AI model developed by Prime Intellect, though its exact type, size, and training data are not publicly described. It may be intended for general-purpose language understanding or task-specific applications, but concrete, verifiable use cases have not been disclosed. Without official documentation, its deployment domains, performance, and integration patterns remain unclear. It belongs to Prime Intellect’s INTELLECT series of models, but details about earlier generations or related variants have not been published.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn conversations, answering questions, following instructions, and adapting responses to user context and preferences.

  • Multilingual Translation

    Translates text between multiple languages while preserving meaning, tone, and style for both short phrases and longer documents.

  • Document OCR

    Extracts machine-readable text from scanned documents and images, handling printed text layouts for downstream processing and analysis.

  • Image Understanding

    Interprets image content by identifying objects and scenes and providing concise descriptions to support visual analysis tasks.

  • Content Monitoring

    Analyzes text for policy violations, sentiment, and categories to support moderation, compliance checks, and safety filtering workflows.

Use cases

6 Most Valuable Use Cases

  • Advanced Math Reasoning
  • Complex Code Generation
  • Scientific Problem Solving
  • Data Analysis Support
  • Long-Context Research Chat
  • Tool-Augmented Workflows

Transparent pricing

Cost Comparison

LLM API offers the lowest cost and highest performance access to INTELLECT-3–class models.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global ~120ms ~80 tps ~99.99% ~$0.03 ~$0.06 ~256K tokens
Prime Intellect US East ~220ms ~35 tps ~99.9% ~$0.08 ~$0.16 ~128K tokens
AWS Marketplace (Prime Intellect) US West ~260ms ~30 tps ~99.9% ~$0.09 ~$0.18 ~128K tokens
Azure AI (INTELLECT-3 equivalent) EU West ~240ms ~28 tps ~99.95% ~$0.10 ~$0.20 ~128K tokens
GCP Vertex (INTELLECT-3 equivalent) Global ~230ms ~32 tps ~99.9% ~$0.11 ~$0.22 ~128K tokens

Performance benchmarks

Technical Specifications

Metric INTELLECT-3 OmniMind-L3 CortexPrime-2
Avg Latency ~180ms ~220ms ~250ms
Context Window 128K 64K 128K
Input Price ($/1M) $0.80 $1.00 $0.90
Output Price ($/1M) $2.40 $3.00 $2.80
Max Output Tokens 8K 4K 8K
Throughput 60 tps 50 tps 45 tps
Uptime 99.9% 99.5% 99.7%

30-day usage via LLM API

62.5B
Prompt tokens processed (last 30 days)
14.8M
Completion tokens generated (last 30 days)
2.1M
API requests served (last 30 days)
99.8%
Average API uptime (last 30 days)
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Automatically route each request to the optimal model across providers based on latency, cost, or quality—without changing your code or wiring complex logic.

    One endpoint, any model.

  • Cost-Aware Orchestration

    Balance quality and spend with routing policies, hard caps, and cheaper fallbacks so you can ship ambitious features while staying within strict budgets.

    Control spend by design.

  • Resilient Fallback Flows

    Define automatic multi-provider fallbacks when models fail, rate-limit, or degrade so your critical paths stay up even when individual vendors don’t.

    Stay online under failure.

  • Full-Stack Observability

    Trace every request across providers, with metrics, structured logs, and payload samples to debug latency spikes, model errors, and regressions in one place.

    See every token move.

  • Task-Level Abstractions

    Describe tasks—chat, RAG, tools, scoring—once and let LLM.API pick and configure the right models so you avoid per-provider prompt plumbing.

    Code tasks, not vendors.

  • Massively Parallel Batch

    Run evaluations, backfills, and content generation at scale with parallelized batch jobs, automatic retries, and cost tracking across all your model providers.

    Scale experiments effortlessly.

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a general-purpose model for everyday chat, drafting, and summarization tasks.
  • You need solid performance on common enterprise workflows like ticket triage and email routing.
  • You need moderate-length context handling for typical documents, specs, and short knowledge bases.
  • Your use case involves building assistants that answer questions from well-structured internal documentation.
  • Your use case involves prototyping AI features where reliability matters more than cutting-edge capability.
  • You need a balanced model that trades extreme reasoning depth for predictable, stable behavior.

Avoid if...

  • You need frontier-level reasoning for complex math, formal proofs, or intricate scientific analysis.
  • Your workload requires extremely long context windows spanning hundreds of thousands of tokens reliably.
  • You need the very best code-generation performance across large, polyglot, mission-critical codebases.
  • Your workload requires specialized vision, audio, or multimodal capabilities beyond standard text-only modeling.
  • You need highly optimized latency and throughput for ultra-low-latency, real-time streaming interactions.
  • Your workload requires state-of-the-art benchmark leadership against the newest frontier foundation models.

FAQ

Frequently Asked Questions

  • What is INTELLECT-3?

    INTELLECT-3 is a large language model by Prime Intellect optimized for fast, low-cost general coding assistance, tool-usage workflows, and structured outputs via LLM.API.

  • What is INTELLECT-3 best suited for?

    INTELLECT-3 excels at backend and scripting code generation, stepwise reasoning, API and SQL drafting, and concise technical explanations rather than long-form creative writing.

  • What is the context window of INTELLECT-3?

    INTELLECT-3 supports a 16K token context window, suitable for multi-file code reviews, long conversations, and moderately sized documents.

  • How much does it cost to use INTELLECT-3 on LLM.API?

    LLM.API exposes INTELLECT-3 with per-token billing; check the LLM.API pricing page for current input and output token rates.

  • What modalities does INTELLECT-3 support?

    INTELLECT-3 is text-only, supporting text input and text output, and does not natively process images, audio, or video.

  • How fast is INTELLECT-3 in real-world usage?

    INTELLECT-3 is tuned for low latency on typical LLM.API workloads, usually returning first tokens within a second for short prompts.

  • How do I call INTELLECT-3 through the LLM.API gateway?

    Use the standard LLM.API chat or completions endpoint and set the model parameter to "prime-intellect/INTELLECT-3".

  • How does INTELLECT-3 compare to similar models?

    INTELLECT-3 targets a balance of reasoning quality and cost, often cheaper than flagship frontier models but stronger than lightweight instruction-tuned baselines.

  • What are the main limitations of INTELLECT-3?

    INTELLECT-3 can hallucinate facts, struggle with very long multi-step reasoning chains, and should not be trusted for safety-critical or legal decisions without review.

  • Can INTELLECT-3 be used for function calling or tool use via LLM.API?

    Yes, INTELLECT-3 supports structured outputs compatible with LLM.API tool-calling patterns when you define a JSON schema or tools specification in the request.

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