Gemma 3 4B vs GLM 5.3 FlashX Legal: which should you choose in 2026?
Gemma 3 4B (by Google) and GLM 5.3 FlashX Legal (by Z.AI) 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.
Gemma 3 4B is about 9.4× cheaper on a blended 3:1 input/output basis ($0.0625 vs $0.59 per 1M tokens).
GLM 5.3 FlashX Legal has the larger context window (1,048,576 tokens vs 131,072 tokens).
Choose Gemma 3 4B if…
- • lower blended API cost matters for your workload
Choose GLM 5.3 FlashX Legal if…
- • you work with longer documents, transcripts, or codebases
- • you need a model with explicit reasoning support
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 | Gemma 3 4B | GLM 5.3 FlashX Legal |
|---|---|---|
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 | 1303 ±9 | — |
Benchmark sources
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Oct 5, 2026.
Pricing, capabilities, and model facts
| Feature | Gemma 3 4B | GLM 5.3 FlashX Legal |
|---|---|---|
| Context & model facts | ||
| Developer | Z.AI | |
| API provider | Z.ai | |
| Input context | 131,072 tokens | 1,048,576 tokens |
| Maximum output | 16,384 tokens | 131,072 tokens |
| Released | — | — |
| Added to Writingmate | Mar 13, 2025 | Nov 2, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | 2024-08-31 | — |
| Capabilities | ||
| Inputs | Text, Image | Text, Image, Video |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | No | Yes |
| Reasoning | No | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.05 | $0.37 |
| Output (per 1M tokens) | $0.10 | $1.25 |
| Blended 3:1 input/output | $0.0625 | $0.59 |
| 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 Nov 2, 2026.
Why Pay for Multiple Subscriptions?
Comparing Gemma 3 4B from Google with GLM 5.3 FlashX Legal from Z.AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Gemma 3 4B | GLM 5.3 FlashX Legal | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Gemma 3 4B vs GLM 5.3 FlashX Legal FAQ
Which is better, Gemma 3 4B or GLM 5.3 FlashX Legal?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Gemma 3 4B is about 9.4× cheaper on a blended 3:1 input/output basis ($0.0625 vs $0.59 per 1M tokens). GLM 5.3 FlashX Legal has the larger context window (1,048,576 tokens vs 131,072 tokens).
Which model is cheaper to use through an API?
Gemma 3 4B is about 9.4× cheaper on a blended 3:1 input/output basis ($0.0625 vs $0.59 per 1M tokens).
Which model supports more context?
GLM 5.3 FlashX Legal has the larger context window (1,048,576 tokens vs 131,072 tokens).
Can I switch between Gemma 3 4B and GLM 5.3 FlashX Legal?
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 Gemma 3 4B comparisons
Nex-N2.6
Nex AGI
Nex-N2.6 is an agentic model from Nex AGI, built on GLM-5.3-Flash with about 321B parameters.
Inkling 2 Small
Thinking Machines
Inkling 2 Small is an open-weight multimodal mixture-of-experts reasoning model from Thinking Machines Lab, with 17B active parameters out of 594B total.
Particle-1
Particle ai
Particle-1 is a token-efficient agentic coding model from Particle AI, post-trained on DeepSeek V4.1 Flash.
GPT-6.1 Terra Pro
OpenAI
GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with reasoning.mode set to pro for higher-quality responses on complex tasks.
More GLM 5.3 FlashX Legal comparisons
Gemini 4 Hydrogen
Gemini 4 Hydrogen is a model by Google.
Gemini 4 Carbon
Gemini 4 Carbon is a frontier Gemini 4 model from Google for coding, agentic and complex reasoning work.
Muse Ember
Meta
Muse Ember is a model by Meta.
Kimi K3.1 Pro
Moonshot AI
Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model and a post-trained successor to Kimi K3, with a 1M-token context window.
MiniMax M3.1 Flash
MiniMax
MiniMax-M3.1-Flash is a multimodal model from MiniMax.
Glimpse
Elorian
Glimpse is a multimodal visual reasoning model from Elorian, built for visual understanding and complex visual reasoning in the physical world.