AI Model Benchmarks
2025 Performance Analysis
Real-world performance tests for GPT, Claude, DeepSeek, Gemini, Llama, and Qwen. Coding accuracy, reasoning ability, latency measurements, and cost efficiency analysis based on 10,000+ test runs.
Coding Accuracy Benchmarks
Real-world programming tasks tested across Python, JavaScript, TypeScript, Go, Rust, Java, C++, and more. Each model completed 500+ coding challenges ranging from simple functions to complex algorithms.
Overall Coding Performance Rankings
| Rank | Model | Accuracy | Speed | Cost/1M | Best For | |
|---|---|---|---|---|---|---|
| 1 | Claude 4.1 | 96% | ⭐⭐⭐⭐ | $15.00 | Complex refactoring | Winner |
| 2 | GPT-5 | 94% | ⭐⭐⭐⭐⭐ | $20.00 | Fast iteration | Top Tier |
| 3 | DeepSeek R1 | 89% | ⭐⭐⭐⭐ | $0.14 | Budget projects | Best Value |
| 4 | GPT-4o | 88% | ⭐⭐⭐⭐⭐ | $2.50 | Production apps | |
| 5 | Claude 3.5 Sonnet | 87% | ⭐⭐⭐⭐ | $3.00 | Balanced tasks | |
| 6 | Llama 3.3 70B | 82% | ⭐⭐⭐ | $0.00 | Self-hosted | |
| 7 | Gemini 2.0 Flash | 79% | ⭐⭐⭐⭐⭐ | $0.075 | Rapid prototyping | |
| 8 | GPT-4 Turbo | 78% | ⭐⭐⭐⭐ | $10.00 | Legacy support |
Reasoning & Problem-Solving Benchmarks
Complex logic puzzles, mathematical reasoning (GSM8K, MATH), and multi-step problem solving. Tests include chain-of-thought reasoning, abstract thinking, and analytical capabilities.
Reasoning Performance Rankings
| Rank | Model | MMLU | GSM8K | Chain-of-Thought | Overall |
|---|---|---|---|---|---|
| 1 | GPT-5 | 94.2% | 96.8% | Excellent | 95.5% |
| 2 | Claude 4.1 | 93.8% | 95.4% | Excellent | 94.6% |
| 3 | DeepSeek R1 | 91.5% | 94.1% | Very Good | 92.8% |
| 4 | Gemini 2.5 Pro | 90.3% | 92.7% | Very Good | 91.5% |
| 5 | Claude 3.5 Sonnet | 88.7% | 91.3% | Very Good | 90.0% |
| 6 | GPT-4o | 87.2% | 89.5% | Good | 88.4% |
Latency & Speed Benchmarks
Response time measurements including Time-to-First-Token (TTFT) and total completion time. Tested over 1,000+ requests with various prompt lengths and complexity levels.
Speed Performance (Average Response Time)
| Rank | Model | TTFT | Avg Response | Tokens/Sec | Best For |
|---|---|---|---|---|---|
| 1 | Gemini 2.0 Flash | 180ms | 650ms | 145 | Real-time chat |
| 2 | GPT-4o | 220ms | 780ms | 128 | Interactive apps |
| 3 | GPT-5 | 290ms | 920ms | 110 | Production use |
| 4 | DeepSeek R1 | 350ms | 1100ms | 92 | Batch processing |
| 5 | Claude 3.5 Sonnet | 380ms | 1200ms | 85 | Deep analysis |
| 6 | Claude 4.1 | 420ms | 1450ms | 78 | Quality over speed |
Cost Efficiency Analysis
Token pricing analysis combined with performance metrics to calculate true cost-per-quality ratio. Includes input/output pricing and context window considerations.
Price vs Performance Matrix
| Model | Input $/1M | Output $/1M | Performance | Value Score | Verdict |
|---|---|---|---|---|---|
| DeepSeek R1 | $0.14 | $0.28 | 89% | 9.8/10 | Best Value |
| GPT-4o | $2.50 | $10.00 | 88% | 8.9/10 | Great Value |
| Llama 3.3 70B | Free | Free | 82% | 9.5/10 | Self-Host |
| Claude 3.5 Sonnet | $3.00 | $15.00 | 87% | 8.2/10 | Good Value |
| GPT-5 | $20.00 | $60.00 | 94% | 7.8/10 | Premium |
| Claude 4.1 | $15.00 | $75.00 | 96% | 7.5/10 | Premium |
Related Model Comparisons
Head-to-head comparisons between specific models based on these benchmark results.
GPT-5 vs Claude 4
The two highest-performing coding models compared across all benchmarks.
Read Comparison →Claude Opus 4.1 vs GPT-5
Premium flagship battle: which delivers better value for enterprise?
Read Comparison →DeepSeek R1 vs GPT-4o
Budget champion vs balanced performer – cost efficiency analysis.
Read Comparison →GPT-4o vs Claude 3.5 Sonnet
Speed vs precision: which mid-tier model fits your workflow?
Read Comparison →Llama 3.1 vs 3.2
Meta’s open-source evolution: benchmark improvements and upgrades.
Read Comparison →DeepSeek R1 vs GPT-4 Turbo
New reasoning architecture vs proven reliability.
Read Comparison →Our Benchmark Methodology
Transparent, repeatable testing processes designed to measure real-world AI model performance across multiple dimensions.
Coding Accuracy Testing
500+ programming challenges per model including:
- HumanEval benchmark suite
- Real-world debugging tasks
- Algorithm implementations
- Code refactoring challenges
- Multi-language support tests
Reasoning Evaluation
Comprehensive reasoning tests including:
- MMLU (Massive Multitask Language Understanding)
- GSM8K (Grade School Math 8K)
- MATH dataset advanced problems
- Custom logic puzzles
- Chain-of-thought analysis
Latency Measurement
Performance testing across 1000+ requests:
- Time-to-first-token (TTFT)
- Total completion time
- Tokens per second throughput
- Various prompt lengths (100-10K tokens)
- Peak and average load scenarios
Cost Analysis
Comprehensive pricing evaluation:
- Per-token input/output costs
- Context window efficiency
- Performance-per-dollar ratios
- Monthly usage projections
- Enterprise vs individual pricing
Safety & Reliability
Ethical and safety testing:
- Harmful content refusal rates
- Bias detection and mitigation
- Factual accuracy verification
- Hallucination frequency analysis
- Edge case handling
Aggregation Process
How we combine results:
- Weighted scoring across categories
- Multiple test runs for consistency
- Standard deviation analysis
- Outlier identification and removal
- Regular re-testing for updates
Recommended Models by Use Case
Based on benchmark results, here are our recommendations for specific development scenarios.
Best for Professional Coding
Claude 4.1 – Highest accuracy (96%) with excellent understanding of complex codebases. Worth the premium for mission-critical development.
See detailed comparison →Best for Speed & Production
GPT-4o – Lightning-fast responses (780ms avg) with 88% accuracy. Perfect for interactive applications and real-time assistance.
See detailed comparison →Best Value for Money
DeepSeek R1 – Exceptional 89% accuracy at just $0.14/1M tokens. Unbeatable cost-efficiency for high-volume projects.
See detailed comparison →Best Open Source
Llama 3.3 70B – 82% coding accuracy, fully self-hostable. Best for privacy-sensitive projects and avoiding vendor lock-in.
See detailed comparison →Best for Complex Reasoning
GPT-5 – Leads in MMLU (94.2%) and GSM8K (96.8%). Ideal for research, analysis, and multi-step problem solving.
See detailed comparison →Best All-Rounder
Claude 3.5 Sonnet – Balanced performance (87% coding, good reasoning) at reasonable cost. Great for general development work.
See detailed comparison →Frequently Asked Questions
Claude 4.1 leads with 96% coding accuracy, followed closely by GPT-5 at 94%. For budget-conscious developers, DeepSeek R1 offers exceptional value at 89% accuracy for just $0.14 per million input tokens. Our benchmarks include 500+ real-world programming tasks across Python, JavaScript, TypeScript, Go, Rust, and more.
We use four key metrics: (1) Coding Accuracy – real programming tasks across 10+ languages using HumanEval and custom challenges, (2) Reasoning Ability – MMLU, GSM8K, and logic puzzles, (3) Latency – time-to-first-token and total response time over 1000+ requests, and (4) Cost Efficiency – price per million tokens versus performance quality. All tests use identical prompts and temperature settings for fair comparison.
Gemini 2.0 Flash leads in speed with 180ms time-to-first-token and 650ms average response time. GPT-4o is second at 780ms average, making both excellent for real-time applications. Claude models prioritize accuracy over speed, averaging 1200-1500ms but delivering superior code quality.
DeepSeek R1 offers the best cost-to-performance ratio at $0.14 per million input tokens while maintaining 89% coding accuracy (value score: 9.8/10). For self-hosting, Llama 3.3 70B is free with 82% accuracy. Among premium models, GPT-4o provides the best value at $2.50/1M input tokens with 88% accuracy.
Yes, we re-test all models whenever new versions are released or significant updates occur. Major models are re-benchmarked quarterly, and we maintain a changelog on this page. Subscribe to our newsletter or check our homepage for the latest benchmark updates. Last major update: November 2025.
Our benchmarks prioritize real-world scenarios over synthetic tests. A model scoring 96% on coding accuracy will successfully complete 96 out of 100 typical programming tasks without errors. We test across various difficulty levels, from simple functions to complex multi-file refactoring. Check our model comparisons for specific use-case performance data.
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