We compare the major LLM APIs as of May 2026 — Google Gemini 2.0 / OpenAI GPT-4o & o1 / Anthropic Claude Opus 4 & Sonnet 4 / Meta Llama 3.3. Code generation, reasoning, multimodal, pricing, and how to use them through each cloud — it's all here.
| Model | Context | Input ($/M tok) | Output ($/M tok) | Multimodal |
|---|---|---|---|---|
| Gemini 2.0 Flash | 1M | $0.075 | $0.30 | Text + image + audio + video |
| Gemini 2.0 Pro | 2M | $1.25 | $5 | Text + image + audio + video (2h) |
| GPT-4o mini | 128k | $0.15 | $0.60 | Text + image |
| GPT-4o | 128k | $2.50 | $10 | Text + image + audio |
| OpenAI o1 | 200k | $15 | $60 | Text |
| Claude Haiku 4 | 200k | $0.80 | $4 | Text + image |
| Claude Sonnet 4 | 200k | $3 | $15 | Text + image |
| Claude Opus 4 | 1M (extended) | $15 | $75 | Text + image |
| Llama 3.3 70B | 128k | OSS (endpoint cost) | Same as input | Text |
| Benchmark | Gemini 2.0 Pro | GPT-4o | Claude Sonnet 4 | Llama 3.3 70B |
|---|---|---|---|---|
| MMLU (general knowledge) | ~85 | ~88 | ~89 | ~86 |
| HumanEval (code) | ~85 | ~90 | ~95 (Sonnet 4) | ~80 |
| SWE-bench (real-world code fixes) | ~50 | ~55 | ~70 (Opus 4) | ~30 |
| MATH | ~75 | ~80 (o1: 95) | ~85 | ~70 |
| Multimodal (image) | ~85 | ~85 | ~80 | — |
| Model | Vertex AI | AWS Bedrock | Azure OpenAI / AI Foundry |
|---|---|---|---|
| Gemini | ◎ | — | — |
| GPT-4o / o1 | — | — | ◎ |
| Claude | ◎ | ◎ | — |
| Llama | ◎ | ◎ | ○ |
| Mistral | ◎ | ◎ | ◎ |
| Use case | Recommended | Why |
|---|---|---|
| High-volume chatbots (low cost) | Gemini Flash | Cheapest at $0.075/M tok |
| Coding agents | Claude Sonnet 4 / Opus 4 | Industry-leading SWE-bench score |
| Math and complex reasoning | OpenAI o1 | Built-in Chain-of-Thought |
| Multimodal video analysis | Gemini 2.0 Pro | 2-hour video in a single request |
| Voice interaction | GPT-4o | Real-time voice |
| RAG (large document sets) | Gemini Pro 2M tok | Long context window |
| Self-hosting | Llama 3.3 / Gemma 2 | OSS |
| Model | Price | Dimensions | Multilingual |
|---|---|---|---|
| OpenAI text-embedding-3-small | $0.02/M tok | 1536 | ◎ |
| OpenAI text-embedding-3-large | $0.13/M tok | 3072 | ◎ |
| Google text-embedding-005 | $0.025/1000 chars | 768 | ◎ |
| Cohere embed-multilingual-v3 | $0.10/M tok | 1024 | ◎ |
Which should I choose: Gemini, GPT, or Claude?
Cost-focused → Gemini Flash; code generation → Claude Sonnet 4; complex reasoning → GPT-4o / o1 / Claude Opus 4; multimodal video → Gemini Pro.
What stands out about Claude Opus 4 (1M context)?
Released 2025-09 with industry-leading coding ability, reasoning accuracy, and a 1M-token context window. You can fit an entire large codebase into a single request.
What are the strengths of Gemini 2.0 Pro?
Up to a 2M-token context window, a single request can process 2 hours of video, Google Search Grounding, and a low price ($1.25/M tok). Best-in-class multimodal.
What are the strengths of GPT-4o / o1?
GPT-4o supports real-time voice and video; o1 uses Chain-of-Thought reasoning for strong math and code; the largest ecosystem thanks to ChatGPT's reach.
How do prices compare?
Flagship comparison (input/output $/M tok): Gemini Pro $1.25/$5, GPT-4o $2.50/$10, Claude Sonnet 4 $3/$15, Claude Opus 4 $15/$75.
Can I use these via GCP / AWS / Azure?
Vertex AI = Gemini + Claude (official Anthropic); Bedrock = Claude + Llama + Mistral; Azure OpenAI = GPT-4o / o1 only. For multi-cloud, Vertex + Azure OpenAI is the recommended combo.
What about open models?
Llama 3.3 (Meta), Mistral Large, Gemma 2 (Google), DeepSeek R1 (reasoning-focused), and Qwen 2.5 (Alibaba). Self-host and fine-tune freely.
Which embedding model should I use for RAG?
OpenAI text-embedding-3-small ($0.02/M tok), Google text-embedding-005, and Cohere embed-multilingual-v3. Google / Cohere lead on multilingual quality; OpenAI wins on cost.
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* Each product is owned by its respective trademark holder (Google / OpenAI / Anthropic / Meta). Check each vendor's official site for the latest specs and pricing. Benchmark scores reflect typical evaluations at publication time and vary by use case.
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