Head-to-head · frontier vs frontier

Claude Sonnet 4.5 Turbo vs o4: which model should you choose?

We compared Claude Sonnet 4.5 Turbo and o4 on price, context window and 12 benchmarks. Claude Sonnet 4.5 Turbo has the stronger average benchmark score; o4 is cheaper on a blended 3:1 basis.

Anthropic

Claude Sonnet 4.5 Turbo

Claude Sonnet 4.5 Turbo is a frontier model from Anthropic. It offers a 1,000,000-token context window, costs $18.37 per million input tokens and $81.65 per million output tokens, and averages 85.9 across the index's benchmark suite.

Input
$18.37 / 1M
Output
$81.65 / 1M
Context
1,000,000
Avg score
85.9
Open weights
No
Full Claude Sonnet 4.5 Turbo page
OpenAI

o4

o4 is a frontier model from OpenAI. It offers a 2,000,000-token context window, costs $17.74 per million input tokens and $66.00 per million output tokens, and averages 85.4 across the index's benchmark suite.

Input
$17.74 / 1M
Output
$66.00 / 1M
Context
2,000,000
Avg score
85.4
Open weights
No
Full o4 page

Specification comparison

SpecClaude Sonnet 4.5 Turboo4Edge
Input price / 1M$18.37$17.74B
Output price / 1M$81.65$66.00B
Context window1,000,0002,000,000B
Max output65,5368,192A
Average score85.985.4A
Arena Elo1461.71469.4B
Open weightsNoNoTie

Benchmark comparison

Claude Sonnet 4.5 Turbo wins 8, o4 wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkClaude Sonnet 4.5 Turboo4GapWinner
MMLU-Pro87.285.31.9Claude Sonnet 4.5 Turbo
GPQA Diamond83.782.31.4Claude Sonnet 4.5 Turbo
SWE-bench Verified75.274.80.4Claude Sonnet 4.5 Turbo
HumanEval90.489.60.8Claude Sonnet 4.5 Turbo
MATH-50088.090.52.5o4
AIME 202587.885.32.5Claude Sonnet 4.5 Turbo
LiveCodeBench78.981.42.5o4
IFEval91.492.81.4o4
TruthfulQA87.785.02.7Claude Sonnet 4.5 Turbo
HellaSwag93.991.02.9Claude Sonnet 4.5 Turbo
MMMU81.180.90.2Claude Sonnet 4.5 Turbo
Arena Elo1461.71469.47.7o4

Price analysis

On a blended 3:1 input-to-output basis, Claude Sonnet 4.5 Turbo costs about $34.19 per million tokens and o4 costs about $29.80. o4 is roughly 13% cheaper than Claude Sonnet 4.5 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

o4 has the larger context window at 2,000,000 tokens, compared with 1,000,000 for Claude Sonnet 4.5 Turbo. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Claude Sonnet 4.5 Turbo wins 8, o4 wins 4, and 0 are tied. On the index average, Claude Sonnet 4.5 Turbo leads o4 by 0.5 points (85.9 versus 85.4).

The price picture favours o4. The context picture favours o4. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.

If you need a single default, start with Claude Sonnet 4.5 Turbo for quality-sensitive work and o4 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose Claude Sonnet 4.5 Turbo if

  • You want the higher average benchmark score in this index.
  • You need the larger 65,536-token output limit.

Choose o4 if

  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 2,000,000-token context window.

FAQ

Is Claude Sonnet 4.5 Turbo better than o4?

On the index average, Claude Sonnet 4.5 Turbo leads o4 by 0.5 points (85.9 versus 85.4). The better choice depends on price, context and your task. Claude Sonnet 4.5 Turbo wins 8 benchmarks, o4 wins 4.

Which is cheaper, Claude Sonnet 4.5 Turbo or o4?

o4 is cheaper on a blended 3:1 basis at about $29.80 per million tokens, versus $34.19 for Claude Sonnet 4.5 Turbo.

Which has the larger context window?

o4 has the larger context window at 2,000,000 tokens. Claude Sonnet 4.5 Turbo has 1,000,000 tokens.

Are these models open weights?

Claude Sonnet 4.5 Turbo is proprietary; o4 is proprietary.

Which should I choose for production?

If you need a single default, start with Claude Sonnet 4.5 Turbo for quality-sensitive work and o4 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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