MiMo-V2.6-Flash API: Endpoint & Pricing
MiMo-V2.6-Flash is an omni-modal chat model that accepts text, images, audio and video and replies in text.
What is MiMo-V2.6-Flash?
MiMo-V2.6-Flash is an omni-modal chat model that accepts text, images, audio and video and replies in text, built by MiMo and served on LLM API under the id mimo-v2.6-flash. It is reachable over the same OpenAI-compatible endpoint as every other model in the catalogue, so switching to it is a one-line change. The prices, context window and provider routing below come straight from the live catalogue and are refreshed every week.
Can you self-host MiMo-V2.6-Flash?
No. We found no public weights for MiMo-V2.6-Flash on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Skip the deploy — use LLM.API as your endpoint
No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.
Providers
LLM.API routes MiMo-V2.6-Flash to the providers below, with discounted effective rates versus list price.
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $0.14; out $0.28 per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.
Try this model
Test MiMo-V2.6-Flash right here — free to start.
Suggestions for your first prompt
Code snippet
Call MiMo-V2.6-Flash through the OpenAI-compatible API — POST /v1/chat/completions.
Switching from OpenAI? Change 2 lines.
- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"Everything else stays the same — SDK, message format, tools, streaming. Set model to mimo-v2.6-flash and the call runs unchanged.
curl https://api.llmapi.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMAPI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mimo-v2.6-flash",
"messages": [
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "medium"
}'from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAPI_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="mimo-v2.6-flash",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LLMAPI_KEY,
baseURL: "https://api.llmapi.ai/v1",
});
const resp = await client.chat.completions.create({
model: "mimo-v2.6-flash",
messages: [
{ role: "user", content: "Give me three crisp launch checklist items." },
],
});
console.log(resp.choices[0].message.content);stream = client.chat.completions.create(
model="mimo-v2.6-flash",
messages=[{"role": "user", "content": "Write a haiku about latency."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True){
"model": "mimo-v2.6-flash",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "medium"
}ENDPOINTS
Supported endpoints & features
Base URL https://api.llmapi.ai/v1 · model ID mimo-v2.6-flash · OpenAI-compatible.
POST /v1/chat/completionsChat Completions — supported
| Feature | Status | How to set it |
|---|---|---|
| Function calling (tools) | Supported | tools, tool_choice |
| Parallel tool calls | Not supported | parallel_tool_calls |
| JSON mode | Supported | response_format: {"type": "json_object"} |
| Structured outputs | Supported | response_format: {"type": "json_schema", …} |
| Streaming | Supported | stream: true |
| Vision (image input) | Supported | image_url content parts |
| Web search | Supported | web_search |
| Reasoning effort | low · medium · high | reasoning_effort: "medium" default medium |
ERRORS
Errors & fallback routing
| Code | What it means | What to do |
|---|---|---|
401 | Missing or invalid API key. | Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active. |
429 | Rate limited, or the upstream provider is throttling the request. | Back off and retry with jitter; the gateway also retries the request on another provider where one is available. |
5xx | Upstream provider error or timeout. | Retry; fallback routing sends the retry to the next healthy provider for this model. |
When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.
Why run MiMo-V2.6-Flash on LLM.API?
Unified AI Routing
Reach MiMo-V2.6-Flash and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale MiMo-V2.6-Flash.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for MiMo-V2.6-Flash alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap MiMo-V2.6-Flash for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You want MiMo-V2.6-Flash over the same OpenAI-compatible chat completions endpoint as the rest of the catalogue
- You want one API key and one bill instead of a separate account per model vendor
- You want provider routing and failover handled for you rather than wired by hand
- Your input is mixed media — text, images, audio or video in one request (MiMo-V2.6-Flash)
Avoid if...
- You must run everything on your own hardware with no cloud inference at all
- You need the weights themselves for fine-tuning — this model is served, not published
- You need a contractual latency or throughput guarantee before benchmarking it yourself
- Your task is embeddings, OCR or media generation rather than chat completions
COMMUNITY
What developers say about MiMo-V2.6-Flash
Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.
- Developers on the NVIDIA forums report achieving 80 tokens per second in code tasks using a 2x DGX Spark setup, though free-flowing prose is significantly slower due to lower draft acceptance rates.
- A developer on Reddit noted the model was usable for compiler and garbage collection development without issues, though other users cautioned that the complex architecture can lead to speed bottlenecks and loops.
- Hardware requirements are a concern; developers note that despite having 15B active parameters, the full 309B sparse MoE architecture requires at least 256GB of memory as all experts must be loaded.
SOURCES
COMPARE
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