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Model comparison

Inkling vs NVIDIA Nemotron 3.5 Lightning

Compare Inkling and NVIDIA Nemotron 3.5 Lightning using the same provider-sourced text & reasoning rubric. No mystery score and no invented benchmark ranking.

Facts checked September 4, 2026

Estimate your cost

Set your usage. Your estimate updates as you type.

Example: 6,000 input tokens for 10 pages, plus a 500-token summary. Page lengths vary; adjust the numbers below.

One run sends your input to the model once and receives an answer. Tokens are pieces of text: input is what you send, output is the answer you receive.

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How this estimate works

Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.

Estimate your cost
ModelEstimated total (USD)
InklingThinking Machines LabNo reviewed rate
NVIDIA Nemotron 3.5 LightningNVIDIANo reviewed rate

Quick take

Inkling

Thinking Machines Lab's large open-weights model for customizable reasoning, coding, tools, vision, and audio workflows.

Best for

  • Teams that want an adaptable open-weight foundation model
  • Agentic coding and tool-use experiments
  • Private or specialized deployments with multimodal inputs

Watch out for

A 975B model is a serious serving project even with sparse activation. Compare the exact hosted or self-hosted route, and do not assume the model's 1M maximum context is available on every provider.

NVIDIA Nemotron 3.5 Lightning

NVIDIA's compact 30B mixture-of-experts model for efficient specialist agents and high-volume text workflows.

Best for

  • High-volume agent sub-tasks
  • Efficient self-hosted reasoning
  • Long-context RAG and instruction workflows

Watch out for

The current release is labeled preview. Validate the exact precision, language, tool template, provider route, and long-context memory needs before standardizing a production fleet.

Compare the published facts

Inkling vs NVIDIA Nemotron 3.5 Lightning

Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.

Text & reasoningInklingNVIDIA Nemotron 3.5 Lightning
Context windowMaximum combined prompt and working context documented by the provider.Up to 1M tokens; Tinker offers 64K and 256KUp to 1M tokens
Maximum outputProvider-published response limit, where available.Not separately publishedNot separately published
Knowledge cutoffLatest reliable knowledge date explicitly published by the model provider. Search and connected tools can retrieve newer information but do not change the model's built-in cutoff.Not publishedPretraining through Sep 2025; post-training through May 2026
Input priceCurrent standard list price per million input tokens unless noted.Hosting dependentFree prototype or deployment cost
Output priceCurrent standard list price per million output tokens unless noted.Hosting dependentFree prototype or deployment cost
InputsMedia types accepted by the listed model endpoint.Text, image, audioText
Tools & agentsSelected native tools and agent-building capabilities, not an exhaustive list.Agentic coding, tools, Python-assisted vision, controllable thinkingAgentic tools and long-running workflows

How to choose

Compare the job, not the hype.

Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.

Inkling

The open weights can be self-hosted so request data stays in infrastructure you control. Tinker and partner-hosted routes have separate retention and training-use terms that must be checked with the selected provider.

NVIDIA Nemotron 3.5 Lightning

Self-hosted weights keep request data under the operator's controls. NVIDIA API trials, OpenRouter, and cloud partners each apply separate logging, retention, and training-use policies.

Frequently asked questions

Inkling vs NVIDIA Nemotron 3.5 Lightning FAQ

What is the main difference between Inkling and NVIDIA Nemotron 3.5 Lightning?

Inkling: Thinking Machines Lab's large open-weights model for customizable reasoning, coding, tools, vision, and audio workflows. NVIDIA Nemotron 3.5 Lightning: NVIDIA's compact 30B mixture-of-experts model for efficient specialist agents and high-volume text workflows.

Should I choose Inkling or NVIDIA Nemotron 3.5 Lightning?

Consider Inkling when your priority is Teams that want an adaptable open-weight foundation model. Consider NVIDIA Nemotron 3.5 Lightning when your priority is High-volume agent sub-tasks. Test both with your own data and provider route before committing.

Is this Inkling vs NVIDIA Nemotron 3.5 Lightning comparison based on Cody benchmarks?

No. This comparison aligns provider-published facts for the Text & reasoning category. It does not claim a universal winner or combine incompatible third-party benchmark scores.