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inclusionAI: Ring-flash-2.0
inclusionai/ring-flash-2.0
Trained on over 20 trillion tokens and refined with supervised fine-tuning and multi-stage reinforcement learning, the model demonstrates strong performance against dense models up to 40B parameters. It excels in complex reasoning, code generation, and frontend development.
Recent activity on Ring-flash-2.0
Tokens processed per day
Thoughput
(tokens/s)
| Providers | Min (tokens/s) | Max (tokens/s) | Avg (tokens/s) |
|---|---|---|---|
| Theta | 39.01 | 178.1 | 138.91 |
First Token Latency
(ms)
| Providers | Min (ms) | Max (ms) | Avg (ms) |
|---|---|---|---|
| Theta | 279 | 838 | 405.56 |
Providers for Ring-flash-2.0
ZenMux Provider to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.
Latency
0.34
s
Throughput
184.46
tps
Uptime
100.00
%
Recent uptime
Oct 10,2025 - 3 PM100.00%
Price
Input
$ 0.28
/ M tokens
Output
$ 2.8
/ M tokens
Cache read
-
Cache write 5m
-
Cache write 1h
-
Cache write
-
Web search
-
Model limitation
Context
128.00K
Max output
32.00K
Supported Parameters
max_completion_tokens
temperature
top_p
frequency_penalty
presence_penalty
seed
-
logit_bias
-
logprobs
top_logprobs
-
response_format
stop
tools
-
tool_choice
-
parallel_tool_calls
-
Model Protocol Compatibility
openai
anthropic
-
Data policy
Prompt training
false
Prompt Logging
Zero retention
Moderation
Responsibility of developer
Sample code and API for Ring-flash-2.0
ZenMux normalizes requests and responses across providers for you.
OpenAI-PythonPythonTypeScriptOpenAI-TypeScriptcURL
pythonfrom openai import OpenAI client = OpenAI( base_url="https://zenmux.ai/api/v1", api_key="<ZenMux_API_KEY>", ) completion = client.chat.completions.create( model="inclusionai/ring-flash-2.0", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image?" } ] } ] ) print(completion.choices[0].message.content)