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DeepSeek V4 Pro vs Saba

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for DeepSeek V4 Pro and Saba.

DeepSeek

DeepSeek V4 Pro

DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...

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Mistral

Saba

Mistral Saba is a 24B-parameter language model specifically designed for the Middle East and South Asia, delivering accurate and contextually relevant responses while maintaining efficient performance. Trained on curated regional...

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Technical Specifications

SpecificationDeepSeek V4 ProSaba
ProviderDeepSeekMistral
Context Window1,048,576 tokens32,768 tokens
Agent Suitability94/100N/A
Time to First Token (TTFT)280 msN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-242025-02-17

API Pricing Comparison

Input Price per Million Tokens

DeepSeek V4 Pro

$0.43

Saba

$0.20

Output Price per Million Tokens

DeepSeek V4 Pro

$0.87

Saba

$0.60

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8850.0%vsN/A
DeepSeek V4 Pro
Saba
HumanEvalPython coding & logic synthesis
8900.0%vsN/A
DeepSeek V4 Pro
Saba
MATHComplex mathematical problem solving
7460.0%vsN/A
DeepSeek V4 Pro
Saba
GPQAGraduate-level expert reasoning
4900.0%vsN/A
DeepSeek V4 Pro
Saba
HellaSwagCommonsense reasoning and inference
8750.0%vsN/A
DeepSeek V4 Pro
Saba
MT-BenchMulti-turn conversation flow quality
918.0%vsN/A
DeepSeek V4 Pro
Saba

DeepSeek V4 Pro Quirks & Gotchas

  • โ–ธMoE architecture โ€” cold-start latency on first request, use keep-alive
  • โ–ธBest cost-performance ratio of any frontier model โ€” strong tool calling for agentic use

Saba Quirks & Gotchas

No developer gotchas reported.