We analyzed the context window performance of 22 leading AI models by testing them using a proprietary 32-message conversation that includes complex synthesis tasks requiring information recall from earlier in the conversation.
Our findings reveal surprising performance patterns that challenge conventional assumptions. Discover which AI context window models truly excel in sustained business conversations and why smaller models often outperform their larger counterparts.
Key AI Models with Notable Context Window Capabilities
- Meta Llama 3.1: Up to 128,000 tokens in some implementations with open-source flexibility but variable performance depending on hosting infrastructure1
- Anthropic Claude 4 Sonnet: 200,000 tokens with consistent performance throughout, showing less than 5% accuracy degradation across the full context window2
- OpenAI GPT-4 Turbo: 128,000 tokens with reliable performance but noticeable slowdown and occasional inconsistencies when approaching maximum capacity3
- Cohere Command-R+: 128,000 tokens optimized for retrieval tasks with specialized architecture for maintaining context coherence4
Context Window Performance Comparison
Model | AI Memory Score | Advertised Context Window Limit (tokens) | Effective Context Window Limit (tokens) |
|---|---|---|---|
gpt-4o | 82.5 | 130k | ~125k |
GPT-4.1 | 87.5 | 1050k | ~1025k |
GPT-4.1 Mini (nano) | 87.5 | 1050k | ~1025k |
Claude Opus 4.1 | 85 | 200k | ~130k |
Claude Opus 4 | 85 | 200k | ~130k |
Claude Sonnet 4 | 82.5 | 1000k | ~510k |
Claude 3.5 Sonnet | 80 | 200k | ~130k |
deepseek-chat-v3.1 | 70 | 165k | ~130k |
gemini-2.5-flash | 67.5 | 1050k | ~1025k |
Claude 3.7 Sonnet | 67.5 | 200k | ~130k |
Ranking Methodology
The ranking of AI models in this table is based AI memory score:
- Indicates how well the model can retain, recall, and use information across sessions.
- Values are taken from benchmark report.
AI Context Window Models and Pricing
Vendor | Representative model | Input ($/1M) | Output ($/1M) |
|---|---|---|---|
Google | Gemini 2.5 Flash(Standard) | $0.30 | $2.50 |
OpenAI | GPT-4o mini | $0.15 | $0.60 |
Anthropic | Claude Sonnet 4(≤200k ctx) | $3.00 | $15.00 |
Cohere | Command-R+ (Aug-2024 rate) | $2.50 | $10.00 |
Mistral | Mistral Medium 3 | $0.40 | $2.00 |
DeepSeek | DeepSeek-V3(“deepseek-chat”) | $0.27 | $1.10 |
xAI | Grok 4 Fast (reasoning) | $0.20(<128k) | $0.50 |
Meta | Llama 4 Scout(served via Together AI) | $0.18 | $0.59 |
Google | Gemini 1.5 Pro | $1.25 (≤128k) / $2.50 (>128k) | $5.00 (≤128k) / $10.00 (>128k) |
- Prices can change and may vary by region, context length, caching/batch options, and special modes (e.g., “thinking”/reasoning).
- All figures are per 1M tokens and shown in USD as of Sep 26, 2025
Detailed Model Profiles
1. OpenAI GPT-4.1 & GPT-4.1 Mini
GPT models offering 1M token context with consistent performance and extensive ecosystem support5 .
Technical Strengths:
- Low measured hallucination rates with improved instruction following capabilities
- Extensive API documentation and third-party integration ecosystem
Technical Limitations:
- Higher per-token pricing compared to open-source alternatives
- API dependency creates vendor lock-in considerations
Technical Characteristics:
- Mini variant offers identical performance at significantly reduced cost
- Robust handling of interference questions without performance degradation
Deployment Considerations: Suitable for applications requiring consistent accuracy across document types, particularly in regulated industries with compliance requirements
2. Meta Llama 4 Scout
Llama models featuring an industry-leading 10M token context and a mixture of experts architecture with open-source flexibility 6 .
Technical Strengths:
- Complete model customization and fine-tuning capabilities
- No recurring API costs after initial deployment
Technical Limitations:
- Requires significant infrastructure investment for optimal performance
- Performance varies significantly based on hosting configuration
Technical Characteristics:
- Mixture of experts (MoE) architecture with 17B active and 109B total parameters
- Native multimodal capabilities with early fusion approach
- Variable hosting options from local deployment to cloud instances
3. Mistral DevStral Medium
DevStral models achieving 61.6% on SWE-Bench Verified with European GDPR compliance and specialized coding capabilities7 .
Technical Strengths:
- State-of-the-art software engineering performance surpassing Gemini 2.5 Pro and GPT 4.1 at quarter the price
- Native GDPR compliance with EU data residency
- Purpose-built for agentic coding with reinforcement learning optimization
- On-premise deployment options for enhanced data privacy
Technical Characteristics:
- 128K token context window optimized for coding workflows
- Available through API at $0.4/M input tokens and $2/M output tokens
- Apache 2.0 license for community building and customization
Deployment Considerations: Suitable for European enterprises requiring GDPR compliance, software development teams, and organizations prioritizing data sovereignty
4. Anthropic Claude Sonnet 4 & Opus 4
Claude models featuring hybrid reasoning with extended thinking modes and conservative safety-focused response patterns.
Technical Strengths:
- Measured low hallucination rates with conservative response patterns
- Advanced memory capabilities with local file access integration
- Tool use during extended thinking for comprehensive analysis
Technical Characteristics:
- 200K-1M token context windows with consistent performance
- Hybrid reasoning approach combining fast and deliberate responses
Deployment Considerations: Appropriate for applications in regulated environments where safety and explainability requirements outweigh maximum context length needs
5. Google Gemini 1.5 Pro & 2.5 Pro
Gemini models provide 2M token capacity with native multimodal processing across text, audio, images, and video8 .
Technical Strengths:
- Native multimodal processing across multiple content formats
- Measured >99% retrieval accuracy in long-context benchmarks
- Context caching for cost optimization on repeated queries
Technical Limitations:
- Response latency increases significantly with very long contexts
- Computationally intensive requiring further latency optimizations
Technical Characteristics:
- Code execution capabilities for dynamic problem solving
- Multiple deployment options through Google Cloud Platform
- Near-perfect retrieval rates across most context ranges
Deployment Considerations: Suitable for applications requiring maximum context length where processing time is less critical than comprehensive document analysis
6. OpenAI GPT-4 Turbo
GPT-4 Turbo offering mature ecosystem support with proven reliability for standard business applications.
Technical Strengths:
- Well-documented performance characteristics from production usage
- Predictable behavior patterns across different use cases
Technical Limitations:
- Context window smaller than newer alternatives (128K vs 1M+ tokens)
- Performance degradation is observed when approaching maximum capacity
Technical Characteristics:
- 128K context window with consistent performance until near-maximum capacity
- 4K output token limit balances response quality with processing speed
- Well-optimized for common business use cases and integrations
Deployment Considerations: Suitable for standard business applications where proven reliability and ecosystem maturity are prioritized over maximum context length
7. xAI Grok-3 & Grok-4
Grok models integrating real-time web search with 2M token context and reinforcement learning-enhanced reasoning9 .
Technical Strengths:
- Real-time information access with native web and X search capabilities
- Advanced reasoning capabilities refined through large scale reinforcement learning
- Native tool use and real-time search integration capabilities
- Specialized training on diverse internet content with current events understanding
Technical Limitations:
- Limited availability requiring X Premium+ subscription
Technical Characteristics:
- 1M-2M token context windows depending on variant
- 256K context window available through API
- Strong performance across academic benchmarks including MMLU and AIME
Deployment Considerations: Suitable for applications requiring real-time information access, social media analysis, and current events tracking
8. DeepSeek-V3 & V3.1
DeepSeek models delivering cost-performance at $0.48 per 1M tokens with hybrid thinking capabilities10 .
Technical Strengths:
- Open-source availability under MIT license
- 164K context window in V3.1 with hybrid thinking capabilities
- Requires only 2.788M H800 GPU hours for full training
Technical Limitations:
- Recommended deployment unit is relatively large, posing a burden for small teams
Technical Characteristics:
- 671B total parameters with 37B activated per token using MoE architecture
- Trained on 14.8 trillion tokens with focus on technical content
- 128K-164K context window with consistent performance across full range
Deployment Considerations: Appropriate for software development, mathematical analysis, research applications, and cost-sensitive deployments requiring high technical capabilities
9. Cohere Command-R+
Command-R models are purpose-built for RAG workflows with specialized enterprise search and multilingual capabilities.
Technical Strengths:
- Purpose-built architecture for retrieval augmented generation (RAG) workflows
- Multi-step tool use capabilities for complex business processes
- Advanced tool use with decision-making capabilities
Technical Characteristics:
- 128K context optimized for information synthesis
- Multilingual support across 10 key business languages
- Safety modes providing granular content control
Deployment Considerations: Suitable for enterprise knowledge management, customer support automation, and multilingual business operations requiring specialized RAG capabilities
FAQ
Key Findings from Our Analysis:
- Context window size alone doesn’t determine performance quality
- Most models show degraded performance in the middle sections of long contexts
- Consistency across the full context range is often more valuable than maximum length
- Cost efficiency varies significantly between models and use cases
Further reading
Reference Links
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