DeepSeek V4 Flash vs GPT-OSS 120B on Groq

Side by side

What three workloads cost

List prices applied to the same work. The cheaper side of each row is in bold.

It depends on the shape of the workload: DeepSeek V4 Flash is cheaper for chat assistant and generation-heavy, GPT-OSS 120B on Groq for long prompts or rag. Reasoning models bill their thinking as output tokens, so real output counts run higher than the visible answer; treat the generation-heavy row as a floor for them.

When to choose which

Price, context and speed are measurable; answer quality on your task is not something a table can settle. Both take the same request, so the honest test is to run your own prompts through each.

Measured speed

Medians of three streamed runs on 2026-09-17, public endpoint, a prompt that asks for about 120 words. Time to first token is the first token of any kind, reasoning included; output speed counts every generated token from that point. Reasoning models then think before the visible answer starts: DeepSeek V4 Flash began answering after 20.1 s at default settings, which you can shorten with the reasoning controls on each model page. Expect ±30% with time of day. We did not measure Groq and do not quote other people's numbers; read "not measured" as unknown, not slow. Method and raw numbers: speed measurements.

Switching from Groq

Both speak the OpenAI wire format. The edit is the base URL, the key and the model id; then re-run your own prompts, because it is a different model.

import os
from openai import OpenAI

# before: Groq
# client = OpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.environ["GROQ_API_KEY"])
# MODEL = "openai/gpt-oss-120b"

# after: Inference APIs
client = OpenAI(base_url="https://api.inferenceapis.com/v1", api_key=os.environ["INFERENCE_API_KEY"])
MODEL = "deepseek-ai/DeepSeek-V4-Flash"

resp = client.chat.completions.create(model=MODEL, messages=[{"role": "user", "content": "Hello"}], max_tokens=500)

More detail, including Node, LangChain and LiteLLM: switching OpenAI-compatible providers.

FAQ

Which is cheaper, DeepSeek V4 Flash or GPT-OSS 120B on Groq?

It depends on the shape of the workload: DeepSeek V4 Flash is cheaper for chat assistant and generation-heavy, GPT-OSS 120B on Groq for long prompts or rag. The table above uses list prices: DeepSeek V4 Flash $0.19 / 1M tokens input, $0.38 / 1M tokens output; GPT-OSS 120B on Groq $0.15 / 1M tokens input, $0.75 / 1M tokens output.

Can I switch from GPT-OSS 120B on Groq to DeepSeek V4 Flash without rewriting code?

Yes, if you call it through an OpenAI-compatible client. Change the base URL to https://api.inferenceapis.com/v1, swap the API key, and set the model to deepseek-ai/DeepSeek-V4-Flash. Because it is a different model, re-run your prompts and evaluations before moving production traffic.

What limits apply to GPT-OSS 120B on Groq?

Free tier: 30 requests/min, 1,000 requests/day, 8,000 tokens/min, 200,000 tokens/day. Developer-tier upgrades were paused when we checked (Sep 2026). On Inference APIs there are no per-minute or per-day request or token caps; usage draws on a prepaid balance.

Why does DeepSeek V4 Flash use more output tokens than the visible answer?

It is a reasoning model: it thinks before it answers and the thinking is billed as output tokens. Budget for that in output-heavy workloads, and set max_tokens to a few hundred or more so the answer is not cut off.

Spotted a price or limit that has changed? Tell us and we will re-check the provider page.