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Compare/Gemma 4 12B (Reasoning) vs Step 3.5 Flash

Gemma 4 12B (Reasoning)vsStep 3.5 Flash

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

Google

Gemma 4 12B (Reasoning)

Input
$0.1/M
Output
$0.3/M
Speed
109 tok/s
TTFT
2.43s
StepFun

Step 3.5 Flash

Input
$0.1/M
Output
$0.3/M
Speed
148 tok/s
TTFT
3.43s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Step 3.5 Flash
Lower Latency
Gemma 4 12B (Reasoning)
Benchmarks (1-1)
Tie

Pricing Comparison

MetricGemma 4 12B (Reasoning)Step 3.5 Flash
Input ($/M tokens)$0.1$0.1
Output ($/M tokens)$0.3$0.3
Cost for 1M input + 100K output tokens:
Gemma 4 12B (Reasoning)$0.13
Step 3.5 Flash$0.13

Speed Comparison

Output Speed (tokens/s) — higher is better
Gemma 4 12B (Reasoning)
109 tok/s
Step 3.5 Flash
148 tok/s
Time to First Token (seconds) — lower is better
Gemma 4 12B (Reasoning)
2.43s
Step 3.5 Flash
3.43s

Editorial Analysis

Verdict. Gemma 4 12B (Reasoning) and Step 3.5 Flash split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Step 3.5 Flash is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Step 3.5 Flash makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Gemma 4 12B (Reasoning) is strongest on Coding Index (31.0), Intelligence Index (14.2). Step 3.5 Flash leads on Intelligence Index (16.6).

Speed. On throughput, Step 3.5 Flash generates tokens at 148 tok/s versus 109 tok/s — about 26% faster. On time-to-first-token, Gemma 4 12B (Reasoning) responds in 2430ms vs 3430ms, which matters most for chat-style UIs.

Provider. Google and StepFun sell to overlapping but distinct developer audiences: Google tends to ship frontier reasoning models with premium positioning, while StepFun 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): Gemma 4 12B (Reasoning) costs $7.50 ($90/year); Step 3.5 Flash costs $7.50 ($90/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Gemma 4 12B (Reasoning) ≈ $1.10/run, Step 3.5 Flash ≈ $1.10/run. At agent/realtime scale (200M input / 100M output per million requests): Gemma 4 12B (Reasoning) ≈ $50/run, Step 3.5 Flash ≈ $50/run.

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.

Head-to-head deltas

  • Benchmark wins tie exactly at 1–1. The tiebreaker on raw benchmark parity will be price, speed, or capability coverage.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
14.216.6
Coding Index
31.0—
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Gemma 4 12B (Reasoning)1 wins
1 winsStep 3.5 Flash

Frequently Asked Questions

Which is cheaper, Gemma 4 12B (Reasoning) or Step 3.5 Flash?

Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.

Which model performs better on benchmarks?

It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Step 3.5 Flash generates tokens faster at 148 tok/s vs 109 tok/s. Gemma 4 12B (Reasoning) also has lower time-to-first-token (2.43s vs 3.43s).

When should I use Gemma 4 12B (Reasoning) vs Step 3.5 Flash?

Choose based on your priorities: both are similarly priced, both perform similarly on benchmarks, and Step 3.5 Flash for faster generation. For latency-sensitive apps, check the TTFT comparison above.