API cost decision in 10 seconds

NewTernary Bonsai 2 9B vs DeepSeek V4 Flash 0731 (batch)

Pick Ternary Bonsai 2 9B for lower cost; pick DeepSeek V4 Flash 0731 (batch) only if the larger context window matters more.

Page updated: 2026-10-27T20:08:11Z Data confirmed: 2026-10-28T05:08:04Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Ternary Bonsai 2 9B for lower cost; pick DeepSeek V4 Flash 0731 (batch) only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Ternary Bonsai 2 9B is estimated at $0.15 vs $0.28 for DeepSeek V4 Flash 0731 (batch), saving $0.13 (47.3% lower).

Cost-first pickTernary Bonsai 2 9B

Context-first pickDeepSeek V4 Flash 0731 (batch)

Sample savings$0.1347.3%

10x traffic gap$1.3

DeepSeek V4 Flash 0731 (batch) has more context, but Ternary Bonsai 2 9B saves $0.13 on the standard workload. At 10x that traffic, the same price gap is about $1.3. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Ternary Bonsai 2 9B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Ternary Bonsai 2 9B	DeepSeek V4 Flash 0731 (batch)
Input-heavy / RAG	5M input + 500K output	Ternary Bonsai 2 9B	$0.47	$0.72
Balanced workload	1M input + 1M output	Ternary Bonsai 2 9B	$0.21	$0.44
Output-heavy chatbot	1M input + 5M output	Ternary Bonsai 2 9B	$0.73	$1.76

Cheaper input Ternary Bonsai 2 9B $0.08 vs $0.11 / 1M

Ternary Bonsai 2 9B is $0.03 cheaper per 1M input tokens (27.3% lower; 1.38x difference).

Cheaper output Ternary Bonsai 2 9B $0.13 vs $0.33 / 1M

Ternary Bonsai 2 9B is $0.2 cheaper per 1M output tokens (60.6% lower; 2.54x difference).

Larger context DeepSeek V4 Flash 0731 (batch) 262.14K vs 1.05M

DeepSeek V4 Flash 0731 (batch) has 786.43K more context (4x larger).

Sample workload Ternary Bonsai 2 9B $0.15 vs $0.28

Ternary Bonsai 2 9B is $0.13 cheaper on the standard workload (47.3% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Ternary Bonsai 2 9B Calculating… Estimated API cost

DeepSeek V4 Flash 0731 (batch) 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

Ternary Bonsai 2 9B has the lower input price; Ternary Bonsai 2 9B has the lower output price; DeepSeek V4 Flash 0731 (batch) offers the larger context window. For the 1M input plus 500K output sample, Ternary Bonsai 2 9B is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.15 for Ternary Bonsai 2 9B and $0.28 for DeepSeek V4 Flash 0731 (batch).

Best Fit

Choose Ternary Bonsai 2 9B when you care most about lower input-token price, and lower output-token price.

Choose DeepSeek V4 Flash 0731 (batch) when you care most about larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, Ternary Bonsai 2 9B is estimated at $0.15 vs $0.28 for DeepSeek V4 Flash 0731 (batch), saving $0.13 (47.3% lower).
Ternary Bonsai 2 9B is $0.13 cheaper on the standard workload (47.3% lower).
Ternary Bonsai 2 9B is $0.03 cheaper per 1M input tokens (27.3% lower; 1.38x difference).
Ternary Bonsai 2 9B is $0.2 cheaper per 1M output tokens (60.6% lower; 2.54x difference).
DeepSeek V4 Flash 0731 (batch) has 786.43K more context (4x larger).

Head-to-Head Specs

Feature	NewTernary Bonsai 2 9B (PrismML)	DeepSeek V4 Flash 0731 (batch) (DeepSeek)
Input Price prompt tokens per 1M	$0.08	$0.11
Completion Price per 1M tokens	$0.13	$0.33
Sample Workload Cost 1M input + 500K output	$0.15	$0.28
Context Window	262.14K	1.05M
Release Date	2026-10-16	2026-07-31

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Ternary Bonsai 2 9B	On the standard 1M input plus 500K output workload, Ternary Bonsai 2 9B is estimated at $0.15 vs $0.28 for DeepSeek V4 Flash 0731 (batch), saving $0.13 (47.3% lower).
High-volume input processing	Ternary Bonsai 2 9B	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Ternary Bonsai 2 9B	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	DeepSeek V4 Flash 0731 (batch)	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

DeepSeek V4 Flash 0731 (free) can replace DeepSeek V4 Flash 0731 (batch) when lower sample workload cost matters most: $0.
DeepSeek V4 Flash (free) can replace DeepSeek V4 Flash 0731 (batch) when lower sample workload cost matters most: $0.
DeepSeek V4.1 Flash (batch) can replace DeepSeek V4 Flash 0731 (batch) when lower sample workload cost matters most: $0.12.
DeepSeek V4 Flash Latest can replace DeepSeek V4 Flash 0731 (batch) when lower sample workload cost matters most: $0.13.

Larger context near this budget

Llama 4 Scout offers 1.31M context with $0.25 sample workload cost.
MiMo-V2.6-Flash offers 1.05M context with $0.28 sample workload cost.
GPT-6 Luna Pro (batch) offers 1.05M context with $0.17 sample workload cost.
GPT-6 Luna (batch) offers 1.05M context with $0.17 sample workload cost.

Popular competitors

Kimi K3.1 Pro · MoonshotAI · #1
DeepSeek V4.1 Flash 1017 · DeepSeek · #2
DeepSeek V4.1 Flash · DeepSeek · #3
GLM 5.3 Flash · Z.ai · #4

Cheaper alternatives

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Larger context alternatives

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Provider catalogs

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PrismML catalog

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DeepSeek catalog

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Ternary Bonsai 2 9B

Ternary Bonsai 2 9B is PrismML’s compact, open-weight language model for conversational AI, reasoning, and tool use. Built on Qwen3.5, it uses ternary weights packed into 2 bits for efficient...

DeepSeek V4 Flash 0731 (batch)

DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....