Every model has genuine trade-offs. This isn't a benchmarks page — benchmarks rarely match real-world performance for your specific tasks. Instead, here's an honest assessment of where each model actually excels and where it falls short.
Quick Reference
| Use Case | Best Choice | Runner-Up |
|---|---|---|
| Complex reasoning / analysis | Claude Opus 4.6 | GPT-4o |
| Production coding | Claude Sonnet 4.6 | GPT-4o |
| Structured data extraction | GPT-4o (structured outputs) | Gemini Flash |
| Long document analysis (>100K tokens) | Gemini 2.0 Pro | Claude (200K) |
| Real-time grounded search | Gemini (with grounding) | — |
| Multimodal (image + text) | GPT-4o or Gemini 2.0 | Claude |
| Local / private inference | LLaMA 3.3 70B | Mistral |
| Cost-optimized production | Gemini Flash | Mistral Small |
| Agentic / tool use | GPT-4o or Claude | — |
| Creative writing | Claude | GPT-4o |
Coding
Claude Sonnet 4.6 and GPT-4o are the top choices for code generation. Both handle complete function implementation, debugging, code review, and architecture questions well.
Where they differ:
- Claude produces cleaner, better-commented code and handles nuanced architectural questions more holistically
- GPT-4o integrates better with the OpenAI ecosystem (function calling, Assistants API, Code Interpreter)
- Codestral (Mistral) is the best open-source option specifically optimized for code, with fill-in-the-middle support
- LLaMA 3.3 70B handles most coding tasks well at zero marginal cost for local inference
For complex algorithmic problems requiring careful reasoning: Claude Opus 4.6 with extended thinking or o1/o3 (OpenAI reasoning models) outperform standard models.
Long Document Analysis
Gemini 2.0 Pro handles the largest context (1M tokens) and does it well for structured analysis tasks. If you genuinely need to process entire books, large codebases, or document collections in one pass, Gemini is the practical choice.
Claude (200K context) is a strong second option and often produces more nuanced analysis on complex documents.
The reality: For most real-world "long document" tasks (10K–50K tokens), any frontier model works. The 1M token window becomes genuinely necessary for edge cases like processing entire legal contracts, analyzing entire codebases, or synthesizing hundreds of research papers.
Structured Data Extraction
GPT-4o's structured outputs API is the most reliable option for guaranteed schema conformance. When you have a JSON schema and need the model to produce output that exactly matches it — no validation required — GPT-4o structured outputs is the practical winner.
Gemini with JSON mode is a close second. Claude handles structured extraction well with clear prompt instructions and XML delimiters but doesn't have the same schema-enforcement guarantees.
For high-volume extraction pipelines where parsing failures are costly: GPT-4o structured outputs significantly reduces error rates.
Multimodal Reasoning
Both GPT-4o and Gemini 2.0 handle images natively and well. The difference:
- GPT-4o excels at image understanding + structured output (extract data from a chart → JSON)
- Gemini 2.0 handles more modalities (audio, video) and longer multi-image context
- Claude supports images but has less differentiated vision capability
For video analysis specifically, Gemini is the practical choice — it's the only mainstream model that handles video as a first-class input.
Cost Comparison (Approximate, Early 2026)
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Claude Opus 4.6 | ~$15 | ~$75 |
| Claude Sonnet 4.6 | ~$3 | ~$15 |
| Claude Haiku 4.5 | ~$0.80 | ~$4 |
| GPT-4o | ~$5 | ~$15 |
| GPT-4o mini | ~$0.15 | ~$0.60 |
| Gemini 2.0 Pro | ~$7 | ~$21 |
| Gemini 2.0 Flash | ~$0.10 | ~$0.40 |
| Mistral Large | ~$4 | ~$12 |
| Mistral Small | ~$1 | ~$3 |
| LLaMA 3.3 70B (Groq) | ~$0.59 | ~$0.79 |
| LLaMA 3.3 70B (local) | $0 | $0 |
Prices change frequently — check provider pricing pages for current rates.
The cost insight: Gemini Flash and GPT-4o mini are dramatically cheaper than their flagship siblings. For high-volume tasks where quality doesn't need to be frontier-level, the cost difference is 10–100x. Benchmark your specific task at the cheap tier before defaulting to the expensive one.
Writing and Tone
Claude consistently produces the best writing quality among frontier models — more natural prose, better voice preservation, and less of the generic "AI writing" style. This holds for technical writing, creative writing, and editing tasks.
GPT-4o writes well but tends toward a more formal, slightly generic style by default. It can be tuned with style instructions, but requires more prompting to match Claude's natural output quality.
For writing assistance where quality and voice matter: Claude is the default choice for most writers.
Agentic Use Cases
GPT-4o has the most mature ecosystem for agentic applications: function calling, Assistants API, Code Interpreter, thread management, and the widest library support.
Claude function calling works reliably and its instruction-following makes it a strong choice for complex multi-step tasks. MCP (Model Context Protocol) support is growing.
LLaMA 3 via frameworks like llama.cpp or Ollama can be used with custom tool implementations, but the tooling ecosystem is less mature than the API providers.
How to Choose
Start with: Claude Sonnet 4.6 or GPT-4o. Both handle most tasks well and have competitive pricing. Pick whichever ecosystem you're more familiar with (Anthropic SDK vs. OpenAI SDK).
Add Gemini when: you need 1M context, real-time grounded search, or video analysis.
Add LLaMA/Mistral when: you have data privacy requirements, high volume with cost pressure, or want to fine-tune.
Add reasoning models (o1/o3 or Claude extended thinking) when: you have specific tasks (complex math, competitive programming, careful contract analysis) where standard models fail consistently.
Don't over-engineer your model stack. One well-prompted model handles 90% of use cases.