API cost decision in 10 seconds

Gemma 4 31B vs Rnj 1 Instruct

Pick Rnj 1 Instruct for lower cost; pick Gemma 4 31B only if the larger context window matters more.

Pricing data updated:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Rnj 1 Instruct for lower cost; pick Gemma 4 31B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Rnj 1 Instruct is estimated at $0.22 vs $0.3 for Gemma 4 31B, saving $0.08 (26.2% lower).

Cost-first pickRnj 1 Instruct
Context-first pickGemma 4 31B
Sample savings$0.0826.2%
10x traffic gap$0.8

Gemma 4 31B has more context, but Rnj 1 Instruct saves $0.08 on the standard workload. At 10x that traffic, the same price gap is about $0.8. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors Gemma 4 31B, balanced workload favors Rnj 1 Instruct, and output-heavy chatbot favors Rnj 1 Instruct.

Workload shapeToken mixBetter pickGemma 4 31BRnj 1 Instruct
Input-heavy / RAG5M input + 500K outputGemma 4 31B$0.78$0.82
Balanced workload1M input + 1M outputRnj 1 Instruct$0.49$0.3
Output-heavy chatbot1M input + 5M outputRnj 1 Instruct$1.97$0.9
Cheaper input Gemma 4 31B $0.12 vs $0.15 / 1M

Gemma 4 31B is $0.03 cheaper per 1M input tokens (20% lower; 1.25x difference).

Cheaper output Rnj 1 Instruct $0.37 vs $0.15 / 1M

Rnj 1 Instruct is $0.22 cheaper per 1M output tokens (59.5% lower; 2.47x difference).

Larger context Gemma 4 31B 262.14K vs 32.77K

Gemma 4 31B has 229.38K more context (8x larger).

Sample workload Rnj 1 Instruct $0.3 vs $0.22

Rnj 1 Instruct is $0.08 cheaper on the standard workload (26.2% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Gemma 4 31B Calculating… Estimated API cost
Rnj 1 Instruct Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Gemma 4 31B has the lower input price; Rnj 1 Instruct has the lower output price; Gemma 4 31B offers the larger context window. For the 1M input plus 500K output sample, Rnj 1 Instruct is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.3 for Gemma 4 31B and $0.22 for Rnj 1 Instruct.

Best Fit

Choose Gemma 4 31B when you care most about lower input-token price, and larger context window.

Choose Rnj 1 Instruct when you care most about lower output-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Rnj 1 Instruct is estimated at $0.22 vs $0.3 for Gemma 4 31B, saving $0.08 (26.2% lower).
  • Rnj 1 Instruct is $0.08 cheaper on the standard workload (26.2% lower).
  • Gemma 4 31B is $0.03 cheaper per 1M input tokens (20% lower; 1.25x difference).
  • Rnj 1 Instruct is $0.22 cheaper per 1M output tokens (59.5% lower; 2.47x difference).
  • Gemma 4 31B has 229.38K more context (8x larger).
Head-to-Head Specs
FeatureGemma 4 31B
(Google)
Rnj 1 Instruct
(EssentialAI)
Input Price
prompt tokens per 1M
$0.12$0.15
Completion Price
per 1M tokens
$0.37$0.15
Sample Workload Cost
1M input + 500K output
$0.3$0.22
Context Window262.14K32.77K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionRnj 1 InstructOn the standard 1M input plus 500K output workload, Rnj 1 Instruct is estimated at $0.22 vs $0.3 for Gemma 4 31B, saving $0.08 (26.2% lower).
High-volume input processingGemma 4 31BLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsRnj 1 InstructLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGemma 4 31BA larger context window leaves more room for retrieved passages, conversation history, or source files.

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Provider catalogs

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Google catalog

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EssentialAI catalog

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Gemma 4 31B

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...

Rnj 1 Instruct

Rnj-1 is an 8B-parameter, dense, open-weight model family developed by Essential AI and trained from scratch with a focus on programming, math, and scientific reasoning. The model demonstrates strong performance...