AI Model Comparison

Claude 3.5 Sonnet vs OpenAI o1

Verdict
Claude 3.5 Sonnet vs OpenAI o1: OpenAI o1 scores higher on the MMLU benchmark

Head-to-head specifications

MetricClaude 3.5 SonnetOpenAI o1Difference
MMLU (general capability)88.7%92.3%-3.6%
Context window200K tokens128K tokens
Price (input / output per 1M)$3 / $15$15 / $60
AccessProprietary APIProprietary API
  • OpenAI o1 leads general capability (MMLU 92.3% vs 88.7%).
  • Claude 3.5 Sonnet offers the larger context window, useful for long documents and codebases.

Verdict: Claude 3.5 Sonnet or OpenAI o1?

Our recommendation
Claude 3.5 Sonnet is the clearly stronger overall choice, winning most of the dimensions that matter.

Claude 3.5 Sonnet advantages

  • Context window (+36%)
  • Input cost (+80%)
  • Output cost (+75%)

OpenAI o1 advantages

  • No decisive advantage on the tracked metrics.

Which should you choose?

  • Choose the Claude 3.5 Sonnet if you work with long documents or large codebases.
  • Choose the Claude 3.5 Sonnet if you process large volumes of input and want the lowest cost.

Value for money

Claude 3.5 Sonnet offers more capability per dollar — a better value pick for high-volume use, delivering 4.00× the MMLU-per-cost of the alternative.

Claude 3.5 Sonnet vs OpenAI o1: which should you choose?

Claude 3.5 Sonnet — Anthropic large language model (2024) with a 200K-token context window and an MMLU score of 88.7%.

OpenAI o1 — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 92.3%.

Claude 3.5 Sonnet vs OpenAI o1: OpenAI o1 scores higher on the MMLU benchmark. OpenAI o1 leads general capability (MMLU 92.3% vs 88.7%). Claude 3.5 Sonnet offers the larger context window, useful for long documents and codebases.

Capability and reasoning

On MMLU — a 57-subject benchmark of general knowledge and reasoning — the OpenAI o1 scores 92.3% versus 88.7%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.

Context window

The Claude 3.5 Sonnet handles up to 200K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.

Pricing and access

Claude 3.5 Sonnet is proprietary api and OpenAI o1 is proprietary api. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.

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 Claude 3.5 Sonnet better than the OpenAI o1?

Claude 3.5 Sonnet is the clearly stronger overall choice, winning most of the dimensions that matter. OpenAI o1 leads general capability (MMLU 92.3% vs 88.7%).

What is the main difference between the Claude 3.5 Sonnet and the OpenAI o1?

OpenAI o1 leads general capability (MMLU 92.3% vs 88.7%). Claude 3.5 Sonnet offers the larger context window, useful for long documents and codebases.

Which is better value?

Claude 3.5 Sonnet offers more capability per dollar — a better value pick for high-volume use, delivering 4.00× the MMLU-per-cost of the alternative.

Which should I choose?

Choose the Claude 3.5 Sonnet if you work with long documents or large codebases. Choose the Claude 3.5 Sonnet if you process large volumes of input and want the lowest cost.

Methodology

Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.

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 2026-05-01
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