Compare/Agnes 2.5 Pro Alpha vs DeepSeek V3 (Dec '24)

Agnes 2.5 Pro AlphavsDeepSeek V3 (Dec '24)

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Sapiens AI

Agnes 2.5 Pro Alpha

Input
$0.45/M
Output
$0.9/M
Speed
202 tok/s
TTFT
2.86s
DeepSeek

DeepSeek V3 (Dec '24)

Input
$0.36/M
Output
$0.89/M
Speed
TTFT

Winner by Category

Cheaper
DeepSeek V3 (Dec '24)
Faster (tok/s)
Agnes 2.5 Pro Alpha
Lower Latency
Agnes 2.5 Pro Alpha
Benchmarks (2-0)
Agnes 2.5 Pro Alpha

Pricing Comparison

MetricAgnes 2.5 Pro AlphaDeepSeek V3 (Dec '24)
Input ($/M tokens)$0.45$0.36
Output ($/M tokens)$0.9$0.89
Cost for 1M input + 100K output tokens:
Agnes 2.5 Pro Alpha$0.54
DeepSeek V3 (Dec '24)$0.45

Speed Comparison

Output Speed (tokens/s) — higher is better
Agnes 2.5 Pro Alpha
202 tok/s
DeepSeek V3 (Dec '24)
Time to First Token (seconds) — lower is better
Agnes 2.5 Pro Alpha
2.86s
DeepSeek V3 (Dec '24)

Editorial Analysis

Verdict. Agnes 2.5 Pro Alpha wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, DeepSeek V3 (Dec '24) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). DeepSeek V3 (Dec '24) makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Agnes 2.5 Pro Alpha is strongest on Coding Index (58.8), Intelligence Index (39.7). DeepSeek V3 (Dec '24) leads on Coding Index (23.0), Intelligence Index (14.2).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. Sapiens AI and DeepSeek sell to overlapping but distinct developer audiences: Sapiens AI tends to ship frontier reasoning models with premium positioning, while DeepSeek often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Agnes 2.5 Pro Alpha costs $27.00 ($324/year); DeepSeek V3 (Dec '24) costs $24.15 ($290/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Agnes 2.5 Pro Alpha ≈ $4.05/run, DeepSeek V3 (Dec '24) ≈ $3.58/run. At agent/realtime scale (200M input / 100M output per million requests): Agnes 2.5 Pro Alpha ≈ $180/run, DeepSeek V3 (Dec '24) ≈ $161/run. DeepSeek V3 (Dec '24) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
39.714.2
Coding Index
58.823.0
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Agnes 2.5 Pro Alpha2 wins
0 winsDeepSeek V3 (Dec '24)

Frequently Asked Questions

Which is cheaper, Agnes 2.5 Pro Alpha or DeepSeek V3 (Dec '24)?

DeepSeek V3 (Dec '24) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.49/M tokens vs $0.56/M for Agnes 2.5 Pro Alpha.

Which model performs better on benchmarks?

Agnes 2.5 Pro Alpha wins 2 out of 12 benchmarks compared to 0 for DeepSeek V3 (Dec '24). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Agnes 2.5 Pro Alpha generates tokens faster at 202 tok/s vs — tok/s. Agnes 2.5 Pro Alpha also has lower time-to-first-token (2.86s vs —s).

When should I use Agnes 2.5 Pro Alpha vs DeepSeek V3 (Dec '24)?

Choose based on your priorities: DeepSeek V3 (Dec '24) for lower cost, Agnes 2.5 Pro Alpha for stronger benchmark performance, and Agnes 2.5 Pro Alpha for faster generation. For latency-sensitive apps, check the TTFT comparison above.