Model Comparison
Compare AI models side-by-side to find the best fit for your use case.
The comparison tool helps you evaluate models side-by-side, comparing capabilities, pricing, and performance.
Comparing Models
Quick Comparison
- Navigate to the Model Hub
- Hover over a model card
- Click Compare
- Select up to 4 models to compare
- View the comparison table
Comparison Table
The comparison shows:
| Attribute | Description |
|---|---|
| Provider | Model creator |
| Context Window | Maximum input tokens |
| Input Price | Cost per 1K input tokens |
| Output Price | Cost per 1K output tokens |
| Speed | Response latency |
| Capabilities | Chat, code, vision, etc. |
Key Comparisons
Current rates for each model are shown in the Model Hub.
gpt-5.5 vs claude-opus-4.8
The two flagships. Reach for either when the work is genuinely hard.
| Attribute | gpt-5.5 | claude-opus-4.8 |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Context | 128K | 200K |
| Vision | Yes | Yes |
| Best for | Broad reasoning, multimodal | Agentic coding, long analysis |
Choose gpt-5.5 for multimodal work and broad general capability.
Choose claude-opus-4.8 for code, agent loops, and long documents.
claude-sonnet-4.6 vs gemini-3.5-flash
The everyday workhorses — most conversations should land on one of these.
| Attribute | claude-sonnet-4.6 | gemini-3.5-flash |
|---|---|---|
| Provider | Anthropic | |
| Context | 200K | 1M |
| Best for | Coding, agents | Speed, very long context |
Choose claude-sonnet-4.6 when output quality on code matters most.
Choose gemini-3.5-flash when you need speed, a huge context window, or a lower bill.
gpt-4o-mini vs claude-haiku-4.5 vs gemini-2.5-flash-lite
The cheap tier, for high-volume and simple work.
| Attribute | gpt-4o-mini | claude-haiku-4.5 | gemini-2.5-flash-lite |
|---|---|---|---|
| Context | 128K | 200K | 1M |
| Best for | Cheap general chat | Quality at low cost | Cheapest, longest context |
Choose gemini-2.5-flash-lite for the lowest cost per token.
Choose gpt-4o-mini as a balanced default.
Choose claude-haiku-4.5 when the small model still needs to be careful.
Comparison Criteria
Quality
Evaluate based on:
- Response accuracy
- Instruction following
- Reasoning depth
- Output formatting
Cost
Calculate total cost:
Total Cost = (Input Tokens × Input Price) + (Output Tokens × Output Price)
Speed
Consider for your use case:
- Streaming latency: Time to first token
- Total time: Full response completion
- Throughput: Tokens per second
Context Window
Larger context allows:
- Longer documents
- More conversation history
- Complex multi-part prompts
Benchmark Scores
Common benchmarks:
| Benchmark | Measures |
|---|---|
| MMLU | General knowledge |
| HumanEval | Code generation |
| GSM8K | Math reasoning |
| HellaSwag | Common sense |
Benchmark scores don't always reflect real-world performance. Test models with your specific use cases.
Making a Decision
Decision Framework
- Define requirements: What capabilities do you need?
- Set budget: What's your cost ceiling?
- Test candidates: Try top 2-3 models
- Measure: Evaluate on real tasks
- Choose: Select based on results
If the workload varies
Comparison is worth doing when you have one narrow, high-volume task and want to pin the cheapest model that handles it well.
For a mixed workload, pinning any single model means overpaying on the easy requests and underperforming on the hard ones. Use alvin and let routing decide per message instead.
Related
- Model Hub Overview - Hub introduction
- Discovery - Finding models
- Providers - Provider details