Compare/Gemini 2.5 Pro Preview (May' 25) vs Command A

Gemini 2.5 Pro Preview (May' 25)vsCommand A

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

Google

Gemini 2.5 Pro Preview (May' 25)

Input
$1.25/M
Output
$10/M
Speed
TTFT
Cohere

Command A

Input
$2.5/M
Output
$10/M
Speed
60 tok/s
TTFT
1.86s

Winner by Category

Cheaper
Gemini 2.5 Pro Preview (May' 25)
Faster (tok/s)
Command A
Lower Latency
Command A
Benchmarks (1-0)
Gemini 2.5 Pro Preview (May' 25)

Pricing Comparison

MetricGemini 2.5 Pro Preview (May' 25)Command A
Input ($/M tokens)$1.25$2.5
Output ($/M tokens)$10$10
Cost for 1M input + 100K output tokens:
Gemini 2.5 Pro Preview (May' 25)$2.25
Command A$3.50

Speed Comparison

Output Speed (tokens/s) — higher is better
Gemini 2.5 Pro Preview (May' 25)
Command A
60 tok/s
Time to First Token (seconds) — lower is better
Gemini 2.5 Pro Preview (May' 25)
Command A
1.86s

Editorial Analysis

Verdict. Gemini 2.5 Pro Preview (May' 25) wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.

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

Strengths. Gemini 2.5 Pro Preview (May' 25) is strongest on Intelligence Index (22.7). Command A leads on Intelligence Index (7.5).

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

Provider. Google and Cohere sell to overlapping but distinct developer audiences: Google tends to ship frontier reasoning models with premium positioning, while Cohere 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): Gemini 2.5 Pro Preview (May' 25) costs $187.50 ($2250/year); Command A costs $225.00 ($2700/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Gemini 2.5 Pro Preview (May' 25) ≈ $26.25/run, Command A ≈ $32.50/run. At agent/realtime scale (200M input / 100M output per million requests): Gemini 2.5 Pro Preview (May' 25) ≈ $1250/run, Command A ≈ $1500/run. Gemini 2.5 Pro Preview (May' 25) 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
22.77.5
Coding Index
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Gemini 2.5 Pro Preview (May' 25)1 wins
0 winsCommand A

Frequently Asked Questions

Which is cheaper, Gemini 2.5 Pro Preview (May' 25) or Command A?

Gemini 2.5 Pro Preview (May' 25) is cheaper overall. Its blended price (3:1 input/output ratio) is $3.44/M tokens vs $4.38/M for Command A.

Which model performs better on benchmarks?

Gemini 2.5 Pro Preview (May' 25) wins 1 out of 12 benchmarks compared to 0 for Command A. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Command A generates tokens faster at 60 tok/s vs — tok/s. However, Command A has lower time-to-first-token (1.86s vs —s).

When should I use Gemini 2.5 Pro Preview (May' 25) vs Command A?

Choose based on your priorities: Gemini 2.5 Pro Preview (May' 25) for lower cost, Gemini 2.5 Pro Preview (May' 25) for stronger benchmark performance, and Command A for faster generation. For latency-sensitive apps, check the TTFT comparison above.