API cost decision in 10 seconds

🔥DeepSeek V4 Flash vs 🔥gpt-oss-120b

Pick gpt-oss-120b for lower cost; pick DeepSeek V4 Flash 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 gpt-oss-120b for lower cost; pick DeepSeek V4 Flash only if the larger context window matters more.

On the standard 1M input plus 500K output workload, gpt-oss-120b is estimated at $0.13 vs $0.25 for DeepSeek V4 Flash, saving $0.12 (48.8% lower).

Cost-first pickgpt-oss-120b
Context-first pickDeepSeek V4 Flash
Sample savings$0.1248.8%
10x traffic gap$1.23

DeepSeek V4 Flash has more context, but gpt-oss-120b saves $0.12 on the standard workload. At 10x that traffic, the same price gap is about $1.23. Use the calculator below to replace the sample workload with your own token volume.

Cheaper input gpt-oss-120b $0.126 vs $0.039 / 1M

gpt-oss-120b is $0.09 cheaper per 1M input tokens (69% lower; 3.23x difference).

Cheaper output gpt-oss-120b $0.252 vs $0.18 / 1M

gpt-oss-120b is $0.07 cheaper per 1M output tokens (28.6% lower; 1.4x difference).

Larger context DeepSeek V4 Flash 1.05M vs 131.07K

DeepSeek V4 Flash has 917.5K more context (8x larger).

Sample workload gpt-oss-120b $0.25 vs $0.13

gpt-oss-120b is $0.12 cheaper on the standard workload (48.8% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
DeepSeek V4 Flash Calculating… Estimated API cost
gpt-oss-120b 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

gpt-oss-120b has the lower input price; gpt-oss-120b has the lower output price; DeepSeek V4 Flash offers the larger context window. For the 1M input plus 500K output sample, gpt-oss-120b is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.25 for DeepSeek V4 Flash and $0.13 for gpt-oss-120b.

Best Fit

Choose DeepSeek V4 Flash when you care most about larger context window.

Choose gpt-oss-120b 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, gpt-oss-120b is estimated at $0.13 vs $0.25 for DeepSeek V4 Flash, saving $0.12 (48.8% lower).
  • gpt-oss-120b is $0.12 cheaper on the standard workload (48.8% lower).
  • gpt-oss-120b is $0.09 cheaper per 1M input tokens (69% lower; 3.23x difference).
  • gpt-oss-120b is $0.07 cheaper per 1M output tokens (28.6% lower; 1.4x difference).
  • DeepSeek V4 Flash has 917.5K more context (8x larger).
Head-to-Head Specs
Feature🔥DeepSeek V4 Flash
(DeepSeek)
🔥gpt-oss-120b
(OpenAI)
Input Price
prompt tokens per 1M
$0.126$0.039
Completion Price
per 1M tokens
$0.252$0.18
Sample Workload Cost
1M input + 500K output
$0.25$0.13
Context Window1.05M131.07K
Release Date2026-04-242025-08-05
Popularity Rank
current rank
#4#20

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productiongpt-oss-120bOn the standard 1M input plus 500K output workload, gpt-oss-120b is estimated at $0.13 vs $0.25 for DeepSeek V4 Flash, saving $0.12 (48.8% lower).
High-volume input processinggpt-oss-120bLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsgpt-oss-120bLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workDeepSeek V4 FlashA larger context window leaves more room for retrieved passages, conversation history, or source files.

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DeepSeek V4 Flash

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...

gpt-oss-120b

gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...