Gemma 4 31B vs Kimi K2 Thinking Turbo
Gemma 4 31B (2026) and Kimi K2 Thinking Turbo (2025) are general-purpose language models from Google DeepMind and Moonshot AI. Gemma 4 31B ships a 256k-token context window, while Kimi K2 Thinking Turbo ships a 262K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Gemma 4 31B is safer overall; choose Kimi K2 Thinking Turbo when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 31B | Kimi K2 Thinking Turbo |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | Long context |
| Context window | 256k | 262K |
| Cheapest output | - | - |
| Provider routes | 0 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 4 31B uniquely exposes Multimodal and Function calling in local model data.
- Local decision data tags Gemma 4 31B for RAG, Agents, and Long context.
- Kimi K2 Thinking Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Kimi K2 Thinking Turbo for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 4 31B
Unavailable
No complete token price in local provider data
Kimi K2 Thinking Turbo
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 4 31B and Kimi K2 Thinking Turbo; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Multimodal and Function calling before moving production traffic.
- No overlapping tracked provider route is sourced for Kimi K2 Thinking Turbo and Gemma 4 31B; plan for SDK, billing, or endpoint changes.
- Gemma 4 31B adds Multimodal and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-31 | 2025-11-06 |
| Context window | 256k | 262K |
| Parameters | 31B | — |
| Architecture | - | - |
| License | Open Source | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 4 31B | Kimi K2 Thinking Turbo |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Gemma 4 31B | Kimi K2 Thinking Turbo |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Gemma 4 31B and function calling: Gemma 4 31B. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Gemma 4 31B has no token price sourced yet and Kimi K2 Thinking Turbo has no token price sourced yet. Provider availability is 0 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 4 31B when provider fit are central to the workload. Choose Kimi K2 Thinking Turbo when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Gemma 4 31B or Kimi K2 Thinking Turbo?
Kimi K2 Thinking Turbo supports 262K tokens, while Gemma 4 31B supports 256k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 4 31B or Kimi K2 Thinking Turbo open source?
Gemma 4 31B is listed under Open Source. Kimi K2 Thinking Turbo is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for multimodal input, Gemma 4 31B or Kimi K2 Thinking Turbo?
Gemma 4 31B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Gemma 4 31B or Kimi K2 Thinking Turbo?
Gemma 4 31B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
When should I pick Gemma 4 31B over Kimi K2 Thinking Turbo?
Gemma 4 31B is safer overall; choose Kimi K2 Thinking Turbo when long-context analysis matters. If your workload also depends on provider fit, start with Gemma 4 31B; if it depends on long-context analysis, run the same evaluation with Kimi K2 Thinking Turbo.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.