API cost decision in 10 seconds

Qwen3.5-122B-A10B vs Rnj 1 Instruct

Pick Rnj 1 Instruct for lower cost; pick Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B 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 $1.3 for Qwen3.5-122B-A10B, saving $1.08 (82.7% lower).

Cost-first pickRnj 1 Instruct
Context-first pickQwen3.5-122B-A10B
Sample savings$1.0882.7%
10x traffic gap$10.75

Qwen3.5-122B-A10B has more context, but Rnj 1 Instruct saves $1.08 on the standard workload. At 10x that traffic, the same price gap is about $10.75. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Rnj 1 Instruct stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickQwen3.5-122B-A10BRnj 1 Instruct
Input-heavy / RAG5M input + 500K outputRnj 1 Instruct$2.34$0.82
Balanced workload1M input + 1M outputRnj 1 Instruct$2.34$0.3
Output-heavy chatbot1M input + 5M outputRnj 1 Instruct$10.66$0.9
Cheaper input Rnj 1 Instruct $0.26 vs $0.15 / 1M

Rnj 1 Instruct is $0.11 cheaper per 1M input tokens (42.3% lower; 1.73x difference).

Cheaper output Rnj 1 Instruct $2.08 vs $0.15 / 1M

Rnj 1 Instruct is $1.93 cheaper per 1M output tokens (92.8% lower; 13.9x difference).

Larger context Qwen3.5-122B-A10B 262.14K vs 32.77K

Qwen3.5-122B-A10B has 229.38K more context (8x larger).

Sample workload Rnj 1 Instruct $1.3 vs $0.22

Rnj 1 Instruct is $1.08 cheaper on the standard workload (82.7% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Qwen3.5-122B-A10B 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

Rnj 1 Instruct has the lower input price; Rnj 1 Instruct has the lower output price; Qwen3.5-122B-A10B 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 $1.3 for Qwen3.5-122B-A10B and $0.22 for Rnj 1 Instruct.

Best Fit

Choose Qwen3.5-122B-A10B when you care most about larger context window.

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

Decision Notes
  • On the standard 1M input plus 500K output workload, Rnj 1 Instruct is estimated at $0.22 vs $1.3 for Qwen3.5-122B-A10B, saving $1.08 (82.7% lower).
  • Rnj 1 Instruct is $1.08 cheaper on the standard workload (82.7% lower).
  • Rnj 1 Instruct is $0.11 cheaper per 1M input tokens (42.3% lower; 1.73x difference).
  • Rnj 1 Instruct is $1.93 cheaper per 1M output tokens (92.8% lower; 13.9x difference).
  • Qwen3.5-122B-A10B has 229.38K more context (8x larger).
Head-to-Head Specs
FeatureQwen3.5-122B-A10B
(Qwen)
Rnj 1 Instruct
(EssentialAI)
Input Price
prompt tokens per 1M
$0.26$0.15
Completion Price
per 1M tokens
$2.08$0.15
Sample Workload Cost
1M input + 500K output
$1.3$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 $1.3 for Qwen3.5-122B-A10B, saving $1.08 (82.7% lower).
High-volume input processingRnj 1 InstructLower 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 workQwen3.5-122B-A10BA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Larger context near this budget
Popular competitors
  • No popular competitor is currently available.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

Qwen catalog

Review all tracked Qwen models before deciding whether this matchup is the right shortlist.

Open Qwen models

EssentialAI catalog

Check other EssentialAI models with comparable pricing, context, or release timing.

Open EssentialAI models
Qwen3.5-122B-A10B

The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...

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...