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-3.1-flash-lite
The cheap tier, for high-volume and simple work.
| Attribute | gpt-4o-mini | claude-haiku-4.5 | gemini-3.1-flash-lite |
|---|---|---|---|
| Context | 128K | 200K | 1M |
| Best for | Cheap general chat | Quality at low cost | Cheapest, longest context |
Choose gemini-3.1-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. Leave Alvin selected in Studio and let routing decide per message instead.
Related
- Model Hub Overview - Hub introduction
- Discovery - Finding models
- Providers - Provider details