NVIDIA Nemotron-3 120B vs Kimi K2.7 Code: Reasoning vs Frontier Coding

NVIDIA's Nemotron-3 120B and Moonshot's Kimi K2.7 Code are both open-weights models on AI Space, and both are built for agentic work, but they come at it from different directions. Nemotron is a lean, efficient reasoning model designed for multi-agent systems; Kimi K2.7 Code is a trillion-parameter model built specifically and single-mindedly for software engineering. If you're choosing between them, the question is whether you want the strongest dedicated coder or a cheaper, faster reasoning engine that holds its own across a broader set of agentic tasks.

TL;DR

Kimi K2.7 Code is the stronger dedicated coding model, with the better-confirmed coding and tool-use results and a trillion-parameter scale behind it. Nemotron-3 120B is the leaner, cheaper option: a 120B-total, 12.7B-active hybrid MoE built for agentic reasoning and multi-agent throughput, at roughly half the input cost and a much lower output cost. For pure coding quality, lead with Kimi. For cost-efficient reasoning and high-throughput agent fleets, Nemotron earns its place. Both run with a 256K context window on AI Space.

Nemotron-3 120B overview

Nemotron-3 120B (full name NVIDIA Nemotron 3 Super 120B A12B) is unusual under the hood. It's a hybrid Mamba2-Transformer architecture with a latent mixture-of-experts design, 120.6 billion total parameters with only 12.7 billion active per token. That combination is built for efficiency: the Mamba layers keep long-context processing cheap, and the sparse expert routing keeps the active compute low, which is why it runs fast and prices low for its accuracy. It's a reasoning model with extended thinking, and NVIDIA positions it squarely at agentic and multi-agent applications: planning, tool calling, long-context analysis, and systems where several agents coordinate.

On the independent Artificial Analysis Intelligence Index it scores well above the median for its size class, and its throughput is high, in the range of a couple hundred tokens per second, which matters when you're running many agent steps in sequence. On AI Space it runs with a 256K-token context window (the model is natively capable of more, but it's served at 256K here). It ships under NVIDIA's open model license, which permits commercial use.

A note on coding benchmarks: published coding numbers for Nemotron-3 120B vary across third-party sources, and we couldn't confirm a single reliable SWE-bench or LiveCodeBench figure against NVIDIA's primary report, so we won't print one. What's well-supported is that it's a strong general reasoning model for its size and a capable agentic worker; treat it as a reasoning-first model that codes competently rather than a benchmark-topping dedicated coder.

Kimi K2.7 Code overview

Kimi K2.7 Code is Moonshot AI's frontier coding model, and it's built at a different scale: a one-trillion-parameter mixture-of-experts model with 32 billion parameters active per token. It's a thinking-only model (there's no non-reasoning mode), and it's natively multimodal, with a vision encoder that lets it take image input alongside text, useful when a task involves screenshots, diagrams, or UI mockups. On AI Space it runs with a 256K-token context window.

For coding specifically, it's the stronger of the two on confirmed results. It's reported at around 60.4 on SWE-bench Verified, a high-water mark among open models at its announcement, and it posts strong agentic tool-use numbers (an 81.1 on MCP Mark Verified, ahead of some leading proprietary models). Its function-calling is polished: nested schemas, parallel tool calls, and recovery from tool errors are handled cleanly, which is exactly what custom coding agents and CI-integrated automation lean on. The trade is that it's a large model, so it costs more to run than Nemotron and isn't as cheap to operate at high volume.

Head-to-head

Nemotron is the cheaper of the two to run, and the gap is widest on output. For agent loops that generate a lot of tokens, that adds up.

How to pick

Lead with Kimi K2.7 Code when coding quality is the priority: hard refactors, long agentic SWE tasks, anything with heavy tool use, or work that benefits from vision input. It's the model with the stronger, better-confirmed coding results, and the one to reach for when you want the best output an open model can give you.

Reach for Nemotron-3 120B when:

• You're running many agents or many steps and want lower cost per token, especially on output
• The work is reasoning- and planning-heavy rather than pure code generation
• You're building multi-agent systems where Nemotron is explicitly tuned to coordinate
• Throughput matters and you want a fast model that still reasons well for its size

For a lot of teams the answer is to keep both: Kimi for the heavy coding, Nemotron for the cheaper reasoning and orchestration around it. On AI Space that's a model-field change, not a re-architecture.

Running both on AI Space

Both models are available through AI Space's OpenAI-compatible API on one subscription. List them in your provider config and switch with the picker:

"models": {
  "kimi-k2.7-code": { "name": "Kimi K2.7 Code" },
  "nemotron-3-120b": { "name": "Nemotron-3 120B" }
}

In code it's the model field: kimi-k2.7-code or nemotron-3-120b. Both run on Cloudflare's network across the US, UK, Germany, Japan, and Australia, so your code stays on infrastructure you trust, and both bill at flat monthly rates (Starter $25, Pro $125) with per-user spend ceilings rather than open-ended per-token pricing.

If you want the cheaper end of the lineup compared directly, see Nemotron-3 120B vs GLM-4.7 Flash, or the flagship matchup in GLM-5.2 vs Kimi K2.7 Code. Get started with AI Space and run both on Cloudflare's global network.