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 are interesting. Smaller models often beat their larger counterparts, and most models fail well before their advertised limits.
The chart below shows efficiency ratios—how much of each model’s advertised context window actually works in practice. See our full methodology for testing details.
*GPT-5 received a score of 0 on our effective context window because it failed to find the first needle in the haystack, which is located 2000 tokens deep.
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 & Methodology
We systematically tested each model’s ability to extract specific information from documents of varying lengths to find where performance declines and fails.
Most models break much earlier than advertised. A model claiming 200k tokens typically becomes unreliable around 130k, with sudden performance drops rather than gradual degradation.
Ranking Methodology
Rankings are based on effective context window size how well models retain, recall, and use information across sessions.
AI Memory Score measures how consistently a model recalls information throughout a conversation, not just from the most recent messages. Higher scores mean the model maintains better awareness of earlier context.
Needle-in-a-Haystack testing
This test checks if models can find specific information buried in long documents. Difficulty increases sharply with document length and needle position.
- Haystack: Artificial documents with neutral, varied content at different lengths to prevent repetition patterns
- Needle: A distinct verification code inserted at specific locations, like CODE-A7B9C3D1E5F2
- Task: Find and extract the exact code when asked “What is the verification code?”
Our testing uses three stages:
Exponential ramp testing: Increases context exponentially to quickly find the approximate failure point instead of checking every length.
Binary search refinement: After failure, binary search pinpoints exactly where reliable performance ends.
Position sensitivity analysis: Tests whether needle position affects retrieval success at near-maximum reliable length, exposing “lost-in-the-middle” effects.
Evaluation: Models must respond with the exact format CODE-XXXX. Success is binary; either they find the correct code or they don’t. This eliminates subjective judgment.
AI Context Window Models and Pricing
- 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
Below, you can see the most affordable models based on their effective context windows.
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
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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