Head-to-head · multimodal vs multimodal

DeepSeek-V3.2 Base vs Sonar Pro Max: which model should you choose?

We compared DeepSeek-V3.2 Base and Sonar Pro Max on price, context window and 12 benchmarks. Sonar Pro Max has the stronger average benchmark score; DeepSeek-V3.2 Base is cheaper on a blended 3:1 basis.

DeepSeek

DeepSeek-V3.2 Base

DeepSeek-V3.2 Base is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.05 per million input tokens and $27.10 per million output tokens, and averages 80.2 across the index's benchmark suite.

Input
$5.05 / 1M
Output
$27.10 / 1M
Context
128,000
Avg score
80.2
Open weights
Yes
Full DeepSeek-V3.2 Base page
Perplexity

Sonar Pro Max

Sonar Pro Max is a multimodal model from Perplexity. It offers a 128,000-token context window, costs $14.01 per million input tokens and $69.33 per million output tokens, and averages 81.1 across the index's benchmark suite.

Input
$14.01 / 1M
Output
$69.33 / 1M
Context
128,000
Avg score
81.1
Open weights
No
Full Sonar Pro Max page

Specification comparison

SpecDeepSeek-V3.2 BaseSonar Pro MaxEdge
Input price / 1M$5.05$14.01A
Output price / 1M$27.10$69.33A
Context window128,000128,000Tie
Max output128,0004,096A
Average score80.281.1B
Arena Elo1417.91437.8B
Open weightsYesNoA

Benchmark comparison

DeepSeek-V3.2 Base wins 3, Sonar Pro Max wins 9, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkDeepSeek-V3.2 BaseSonar Pro MaxGapWinner
MMLU-Pro84.383.70.6DeepSeek-V3.2 Base
GPQA Diamond77.178.81.7Sonar Pro Max
SWE-bench Verified69.171.72.6Sonar Pro Max
HumanEval82.885.12.3Sonar Pro Max
MATH-50083.786.22.5Sonar Pro Max
AIME 202574.374.90.6Sonar Pro Max
LiveCodeBench73.175.72.6Sonar Pro Max
IFEval86.988.31.4Sonar Pro Max
TruthfulQA83.582.01.5DeepSeek-V3.2 Base
HellaSwag90.288.02.2DeepSeek-V3.2 Base
MMMU76.777.20.5Sonar Pro Max
Arena Elo1417.91437.819.9Sonar Pro Max

Price analysis

On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Base costs about $10.56 per million tokens and Sonar Pro Max costs about $27.84. DeepSeek-V3.2 Base is roughly 62% cheaper than Sonar Pro Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 3, Sonar Pro Max wins 9, and 0 are tied. On the index average, Sonar Pro Max leads DeepSeek-V3.2 Base by 0.9 points (81.1 versus 80.2).

The price picture favours DeepSeek-V3.2 Base. The context picture is effectively a tie. 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 Sonar Pro Max for quality-sensitive work and DeepSeek-V3.2 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose DeepSeek-V3.2 Base if

  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 128,000-token output limit.
  • You need open weights for self-hosting or fine-tuning.

Choose Sonar Pro Max if

  • You want the higher average benchmark score in this index.

FAQ

Is DeepSeek-V3.2 Base better than Sonar Pro Max?

On the index average, Sonar Pro Max leads DeepSeek-V3.2 Base by 0.9 points (81.1 versus 80.2). The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 3 benchmarks, Sonar Pro Max wins 9.

Which is cheaper, DeepSeek-V3.2 Base or Sonar Pro Max?

DeepSeek-V3.2 Base is cheaper on a blended 3:1 basis at about $10.56 per million tokens, versus $27.84 for Sonar Pro Max.

Which has the larger context window?

Both models list a 128,000-token context window.

Are these models open weights?

DeepSeek-V3.2 Base is open weights; Sonar Pro Max is proprietary.

Which should I choose for production?

If you need a single default, start with Sonar Pro Max for quality-sensitive work and DeepSeek-V3.2 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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