Compare/Gemma 3n E4B Instruct vs Sarvam 30B (high)

Gemma 3n E4B InstructvsSarvam 30B (high)

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

Google

Gemma 3n E4B Instruct

Input
$0.06/M
Output
$0.12/M
Speed
55 tok/s
TTFT
1.50s
Sarvam

Sarvam 30B (high)

Input
$0.03/M
Output
$0.11/M
Speed
TTFT

Winner by Category

Cheaper
Sarvam 30B (high)
Faster (tok/s)
Gemma 3n E4B Instruct
Lower Latency
Gemma 3n E4B Instruct
Benchmarks (1-1)
Tie

Pricing Comparison

MetricGemma 3n E4B InstructSarvam 30B (high)
Input ($/M tokens)$0.06$0.03
Output ($/M tokens)$0.12$0.11
Cost for 1M input + 100K output tokens:
Gemma 3n E4B Instruct$0.07
Sarvam 30B (high)$0.04

Speed Comparison

Output Speed (tokens/s) — higher is better
Gemma 3n E4B Instruct
55 tok/s
Sarvam 30B (high)
Time to First Token (seconds) — lower is better
Gemma 3n E4B Instruct
1.50s
Sarvam 30B (high)

Editorial Analysis

Verdict. Gemma 3n E4B Instruct and Sarvam 30B (high) 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 1.1× the per-million-token cost, Sarvam 30B (high) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Sarvam 30B (high) makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Gemma 3n E4B Instruct is strongest on Coding Index (3.2), Intelligence Index (1.0). Sarvam 30B (high) leads on Intelligence Index (6.4).

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

Provider. Google and Sarvam sell to overlapping but distinct developer audiences: Google tends to ship frontier reasoning models with premium positioning, while Sarvam 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 3n E4B Instruct costs $3.60 ($43/year); Sarvam 30B (high) costs $2.55 ($31/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Gemma 3n E4B Instruct ≈ $0.54/run, Sarvam 30B (high) ≈ $0.37/run. At agent/realtime scale (200M input / 100M output per million requests): Gemma 3n E4B Instruct ≈ $24/run, Sarvam 30B (high) ≈ $17/run. Sarvam 30B (high) 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.

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
1.06.4
Coding Index
3.2
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Gemma 3n E4B Instruct1 wins
1 winsSarvam 30B (high)

Frequently Asked Questions

Which is cheaper, Gemma 3n E4B Instruct or Sarvam 30B (high)?

Sarvam 30B (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.05/M tokens vs $0.07/M for Gemma 3n E4B Instruct.

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?

Gemma 3n E4B Instruct generates tokens faster at 55 tok/s vs — tok/s. Gemma 3n E4B Instruct also has lower time-to-first-token (1.50s vs —s).

When should I use Gemma 3n E4B Instruct vs Sarvam 30B (high)?

Choose based on your priorities: Sarvam 30B (high) for lower cost, both perform similarly on benchmarks, and Gemma 3n E4B Instruct for faster generation. For latency-sensitive apps, check the TTFT comparison above.