AI Model Comparison

o3 vs Claude 4 Sonnet

Verdict
o3 vs Claude 4 Sonnet: o3 scores higher on the Intelligence Index

Head-to-head specifications

Metrico3Claude 4 SonnetDifference
Intelligence Index31.029.0+6.9%
Context window256K tokens1M tokens
Blended price ($/1M tokens)$0.90$1.20-25.0%
AccessProprietary APIProprietary API
  • o3 leads overall capability (Intelligence Index 31.0 vs 29.0).
  • o3 is the cheaper model to run at $0.90/1M blended tokens — about 1.3× cheaper.
  • Claude 4 Sonnet offers the larger context window (1M tokens), useful for long documents and codebases.

Verdict: o3 or Claude 4 Sonnet?

Our recommendation
o3 takes the overall edge, though Claude 4 Sonnet wins in specific areas worth weighing.

o3 advantages

  • General intelligence (+6%)
  • Affordability (+25%)

Claude 4 Sonnet advantages

  • Context window (+74%)

Which should you choose?

  • Choose the o3 if you need the strongest overall reasoning and accuracy.
  • Choose the Claude 4 Sonnet if you work with long documents or large codebases.
  • Choose the o3 if you want the lowest cost per token at scale.

Value for money

o3 offers more intelligence per dollar (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.

o3 vs Claude 4 Sonnet: which should you choose?

o3 — OpenAI multimodal model with an Intelligence Index of 31, a 256K-token context window and a blended price of $0.9/1M tokens.

Claude 4 Sonnet — Anthropic multimodal model with an Intelligence Index of 29, a 1M-token context window and a blended price of $1.2/1M tokens.

o3 vs Claude 4 Sonnet: o3 scores higher on the Intelligence Index. o3 leads overall capability (Intelligence Index 31.0 vs 29.0). o3 is the cheaper model to run at $0.90/1M blended tokens — about 1.3× cheaper.

Capability: intelligence, coding and agentic work

On the composite Intelligence Index the o3 scores 31.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.

Context window and speed

The Claude 4 Sonnet accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once.

Pricing and access

At blended per-token rates, o3 is the cheaper model to run ($0.90 vs $1.20 per 1M tokens). o3 is proprietary api and Claude 4 Sonnet is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.

The verdict

Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.

Frequently asked questions

Is the o3 better than the Claude 4 Sonnet?

o3 takes the overall edge, though Claude 4 Sonnet wins in specific areas worth weighing. o3 leads overall capability (Intelligence Index 31.0 vs 29.0).

What is the main difference between the o3 and the Claude 4 Sonnet?

o3 leads overall capability (Intelligence Index 31.0 vs 29.0). o3 is the cheaper model to run at $0.90/1M blended tokens — about 1.3× cheaper.

Which is better value?

o3 offers more intelligence per dollar (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.

Which should I choose?

Choose the o3 if you need the strongest overall reasoning and accuracy. Choose the Claude 4 Sonnet if you work with long documents or large codebases.

Methodology

Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.

MC
Marcus Chen
Hardware & Product Analyst

Marcus benchmarks processors, GPUs, phones and vehicles and maintains normalized performance databases.

MSc Computer Engineering10+ years review experience
✓ Reviewed by Priya Nair, Data Quality Reviewer.
Last updated 2026-07-01
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