Free to start

MiniMax: MiniMax M2.5 vs NVIDIA: Nemotron 3.5 Super VL

Try both on us. Starter credit covers your first requests, then one key routes MiniMax M2.5 and Nemotron 3.5 Super VL. Switch by changing the model name.

Compare routesProviders, price, context, and policy posture in one view.

Use auto when uptime mattersKeep a primary model and let fallback handle provider failures.

Same API shapeUse the OpenAI client and set the model you want.

Practical read

Monthly evidence

MiniMax: MiniMax M2.5 has more Credits provider routes. NVIDIA: Nemotron 3.5 Super VL has the lower published input-plus-output rate and the larger context window. There is not enough recent probe data to compare speed.

$1.577225/1MMiniMax: MiniMax M2.5 cheapest route

$0.5275/1MNVIDIA: Nemotron 3.5 Super VL cheapest route

not enough dataMiniMax: MiniMax M2.5 fastest measured p50 TTFT

1400 msNVIDIA: Nemotron 3.5 Super VL fastest measured p50 TTFT via io-net

The price comparison adds each route's rate for one million input tokens and one million output tokens; your cost depends on your input/output mix. Prices and live route measurements can change. Use the monthly reports when you need a stable evidence window, then run a small task-specific eval before choosing a production default.

MiniMax: MiniMax M2.5NVIDIA: Nemotron 3.5 Super VL

Model idminimax/minimax-m2.5nvidia/nemotron-3.5-super-vl

Publisher MiniMax NVIDIA

AI IQ IQ 103#111 IQ 105#100

Context196,608 tokens1,048,576 tokens

Credits provider routes64

Cheapest route$1.577225/1M$0.5275/1M

Cached input$0.03165/1M to $0.186208/1M$0.0211/1M to $0.089675/1M

Privacy posturehas ZDR routehas ZDR route

Fastest measured p50 TTFTnot enough data1400 ms via io-net

Highest measured throughputnot enough datanot enough data

Recent route uptime range100.00%50.00% to 100.00%

Modes chat chat

MiniMax: MiniMax M2.5 routes

Overview6 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

NVIDIA: Nemotron 3.5 Super VL routes

Overview4 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

Related comparisons

Browse every comparison

MiniMax: MiniMax M2.5 vs Z.ai: GLM 5.239 combined provider routes
MiniMax: MiniMax M2.5 vs Z.ai: GLM 5.339 combined provider routes
MiniMax: MiniMax M2.5 vs Z.ai: GLM 5.3 Flash38 combined provider routes
NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.237 combined provider routes
NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.337 combined provider routes
NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.3 Flash36 combined provider routes
MiniMax: MiniMax M2.5 vs MoonshotAI: Kimi K334 combined provider routes
DeepSeek: DeepSeek V4 Flash 0731 vs MiniMax: MiniMax M2.532 combined provider routes

Production choice

Pick a default model. Keep fallback enabled.

TrustedRouter is useful when you know the model you want, but still need provider rollover, budget limits, usage records, and a prompt path you can verify.

OpenAI clientPython

client = OpenAI(
    base_url="https://api.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

response = client.chat.completions.create(
    model="minimax/minimax-m2.5",
    messages=messages,
)

Questions

Which should I use, MiniMax: MiniMax M2.5 or NVIDIA: Nemotron 3.5 Super VL?

Is MiniMax: MiniMax M2.5 or NVIDIA: Nemotron 3.5 Super VL cheaper?

The current cheapest TrustedRouter route is $1.577225/1M for MiniMax: MiniMax M2.5 and $0.5275/1M for NVIDIA: Nemotron 3.5 Super VL. The comparison uses current catalog prices and updates as provider pricing changes.

Is MiniMax: MiniMax M2.5 or NVIDIA: Nemotron 3.5 Super VL faster?

Current measured p50 time to first token is not enough data for MiniMax: MiniMax M2.5 and 1400 ms for NVIDIA: Nemotron 3.5 Super VL. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test MiniMax: MiniMax M2.5 and NVIDIA: Nemotron 3.5 Super VL with the same API?

Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between minimax/minimax-m2.5 and nvidia/nemotron-3.5-super-vl. This makes side-by-side evals possible without maintaining two provider integrations.