Gemini 3.1 Flash Lite Preview vs Nemotron 3.5 LightningWhich Is Better in 2026?
Gemini 3.1 Flash Lite Preview vs Nemotron 3.5 Lightning: which should you choose in 2026?
Gemini 3.1 Flash Lite Preview (by Google) and Nemotron 3.5 Lightning (by NVIDIA) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
Nemotron 3.5 Lightning is about 4.1× cheaper on a blended 3:1 input/output basis ($0.1375 vs $0.5625 per 1M tokens).
Gemini 3.1 Flash Lite Preview has the larger context window (1,048,576 tokens vs 262,144 tokens).
Choose Gemini 3.1 Flash Lite Preview if…
- • you work with longer documents, transcripts, or codebases
- • you need image inputs
Choose Nemotron 3.5 Lightning if…
- • lower blended API cost matters for your workload
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Gemini 3.1 Flash Lite Preview | Nemotron 3.5 Lightning |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
| Arena preference scores | ||
Human preference score | 1432 ±4 | — |
Human preference score for code and web development | 1254 ±7 | — |
Benchmark sources
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 12, 2026.
Pricing, capabilities, and model facts
| Feature | Gemini 3.1 Flash Lite Preview | Nemotron 3.5 Lightning |
|---|---|---|
| Context & model facts | ||
| Developer | NVIDIA | |
| API provider | NVIDIA | |
| Input context | 1,048,576 tokens | 262,144 tokens |
| Maximum output | 65,536 tokens | 262,144 tokens |
| Released | — | — |
| Added to Writingmate | Mar 3, 2026 | Aug 11, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text, Image, Video, File, Audio | Text |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | No |
| Reasoning | Yes | Yes |
| Vision | Yes | No |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.25 | $0.10 |
| Output (per 1M tokens) | $1.50 | $0.25 |
| Blended 3:1 input/output | $0.5625 | $0.1375 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Aug 11, 2026.
Why Pay for Multiple Subscriptions?
Comparing Gemini 3.1 Flash Lite Preview from Google with Nemotron 3.5 Lightning from NVIDIA? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Gemini 3.1 Flash Lite Preview | Nemotron 3.5 Lightning | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Gemini 3.1 Flash Lite Preview vs Nemotron 3.5 Lightning FAQ
Which is better, Gemini 3.1 Flash Lite Preview or Nemotron 3.5 Lightning?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Nemotron 3.5 Lightning is about 4.1× cheaper on a blended 3:1 input/output basis ($0.1375 vs $0.5625 per 1M tokens). Gemini 3.1 Flash Lite Preview has the larger context window (1,048,576 tokens vs 262,144 tokens).
Which model is cheaper to use through an API?
Nemotron 3.5 Lightning is about 4.1× cheaper on a blended 3:1 input/output basis ($0.1375 vs $0.5625 per 1M tokens).
Which model supports more context?
Gemini 3.1 Flash Lite Preview has the larger context window (1,048,576 tokens vs 262,144 tokens).
Can I switch between Gemini 3.1 Flash Lite Preview and Nemotron 3.5 Lightning?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
Related comparisons
More Gemini 3.1 Flash Lite Preview comparisons
Muse Spark 1.2
Meta
Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks.
Muse Spark 1.1
Meta
Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks.
MiMo-V2.5
Xiaomi
MiMo-V2.5 is a native omnimodal model by Xiaomi.
Nova 2 Lite
Amazon
Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text.
Sakana Namazu
Sakana AI
Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts.
Qwen3.8 Max
Qwen
Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series, the general-availability successor to the Qwen3.8 Max Preview.
More Nemotron 3.5 Lightning comparisons
Sakana Namazu
Sakana AI
Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts.
Solar Pro 4
Upstage
Solar Pro 4 is a large language model from Upstage.
Muse Glimmer 30B
Meta
Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware.
Muse Spark 1.2
Meta
Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks.
Qwen3.8 Max
Qwen
Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series, the general-availability successor to the Qwen3.8 Max Preview.
DeepSeek V4 Flash 0731
DeepSeek
DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total.