Kimi K3.1 Pro Open vs RERelace Search Paid
Kimi K3.1 Pro vs Relace Search compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Relace Search — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$10 /M outputNo per-token fees if you self-host
$3 /M outputManaged API (no infra to run)
Kimi K3.1 Pro vs Relace Search specs
| Spec | Kimi K3.1 Pro | Relace Search | Winner |
|---|---|---|---|
| Maker | Moonshot AI | Relace | – |
| Type | Open-weight | Proprietary | |
| Context window | 1M tokens | 256K tokens | |
| Input price | $2/M · free self-host | $1/M | RERelace Search |
| Output price | $10/M · free self-host | $3/M | RERelace Search |
| Vision / multimodal | Yes | No | |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | |
| License | Modified MIT | Proprietary |
Price gap & when to choose each
Relace Search is ~3.3× cheaper than Kimi K3.1 Pro on output tokens ($3 vs $10 per M tokens).
- You want to self-host or run on your cloud
- You need the longer 1M context window
- You prioritize data privacy & control
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You want the lower output price
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | Kimi K3.1 Pro | Relace Search |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✗ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✓ | ✗ |
Benchmarks: Kimi K3.1 Pro vs Relace Search
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How Kimi K3.1 Pro and Relace Search score
| Criterion | Kimi K3.1 Pro | Relace Search |
|---|---|---|
| Cost-efficiency | 3.5 | 4.0 |
| Context window | 5.0 | 4.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 3.5 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
What each model is
Kimi K3.1 Pro Open
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. It is built for long-horizon coding, 3D...
Relace Search Paid
The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search performs agentic...
Other models in these families
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Frequently asked questions
Is Kimi K3.1 Pro as good as Relace Search?
Kimi K3.1 Pro is open-weight and competitive on many tasks, but Relace Search may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Can I run Kimi K3.1 Pro locally?
Yes. Kimi K3.1 Pro has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Relace Search is API-only and cannot be self-hosted.
Kimi K3.1 Pro vs Relace Search — which should I pick in 2026?
Choose Kimi K3.1 Pro if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Relace Search if you want frontier capability through a managed API with zero infrastructure to run.
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