Gemini 3.5 Flash Lite vs Jev Router: which should you choose in 2026?
Gemini 3.5 Flash Lite (by Google) and Jev Router (by Typesafe) 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.
Choose Gemini 3.5 Flash Lite if…
- • you need a model with explicit reasoning support
Choose Jev Router if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
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 3.5 Flash Lite | Jev Router |
|---|---|---|
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 | 32.7% Artificial Analysis | — |
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 | — | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 63.9% LiveBench | — |
| Arena preference scores | ||
Human preference score | 1456 ±5 | — |
Human preference score for code and web development | 1439 ±41 | — |
Benchmark sources
- Arena AI leaderboard
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Sep 26, 2026.
Pricing, capabilities, and model facts
| Feature | Gemini 3.5 Flash Lite | Jev Router |
|---|---|---|
| Context & model facts | ||
| Developer | Typesafe | |
| API provider | TypeSafe | |
| Input context | 1,048,576 tokens | 1,000,000 tokens |
| Maximum output | 65,536 tokens | — |
| Released | — | — |
| Added to Writingmate | Jul 21, 2026 | Sep 25, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text, Image, Video, File, Audio | Audio, File, Image, Text, Video |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | No |
| Reasoning | Yes | No |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.30 | — |
| Output (per 1M tokens) | $2.50 | — |
| Blended 3:1 input/output | $0.85 | — |
| 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 Sep 25, 2026.
Why Pay for Multiple Subscriptions?
Comparing Gemini 3.5 Flash Lite from Google with Jev Router from Typesafe? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Gemini 3.5 Flash Lite | Jev Router | 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 3.5 Flash Lite vs Jev Router FAQ
Which is better, Gemini 3.5 Flash Lite or Jev Router?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
Which model is cheaper to use through an API?
A comparable blended token price is not available for both models. Check the API pricing rows for the values that are currently published.
Which model supports more context?
The published context windows are close enough that this page does not treat the difference as a material advantage.
Can I switch between Gemini 3.5 Flash Lite and Jev Router?
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 3.5 Flash Lite comparisons
Muse Spark 1.3 Contributor
Meta
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows.
Muse Spark 1.3
Meta
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows.
Muse Spark 1.2 Contributor
Meta
Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost.
Perceptron Mk1.5
Perceptron
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents.
MiMo-V2.6-Pro-UltraSpeed
Xiaomi
MiMo-V2.6-Pro-UltraSpeed is the fast speed edition of Xiaomi's flagship foundation model, MiMo-V2.6-Pro.
MiMo-V2.6-Flash
Xiaomi
MiMo-V2.6-Flash is an open-source foundation model developed by Xiaomi.
More Jev Router comparisons
Muse Spark 1.3 Contributor
Meta
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows.
Muse Spark 1.3
Meta
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows.
Gemini 3.8 Flash
Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.
Muse Spark 1.2 Contributor
Meta
Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost.
Gemini 3.7 Flash
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning.
Gemini 3.6 Flash
Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development.