Inkling Small vs Morph V3 Large compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Morph V3 Large — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
| Spec | Inkling Small | Morph V3 Large | Winner |
|---|---|---|---|
| Maker | Thinkingmachines | Morph | – |
| Type | Open-weight | Proprietary | THInkling Small |
| Context window | 1M tokens | 262K tokens | THInkling Small |
| Input price | $0.45/M · free self-host | $0.9/M | THInkling Small |
| Output price | $1.2/M · free self-host | $1.9/M | THInkling Small |
| Vision / multimodal | Yes | No | THInkling Small |
| Tool / function calling | Yes | No | THInkling Small |
| Self-hostable | Yes | No (API only) | THInkling Small |
| License | Open weights | Proprietary | THInkling Small |
Inkling Small is ~1.6× cheaper than Morph V3 Large on output tokens ($1.2 vs $1.9 per M tokens).
| Capability | Inkling Small | Morph V3 Large |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✗ |
| Tool / function calling | ✓ | ✗ |
| 1M+ context window | ✓ | ✗ |
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
| Criterion | Inkling Small | Morph V3 Large |
|---|---|---|
| Cost-efficiency | 4.5 | 4.5 |
| Context window | 5.0 | 4.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 2.0 |
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.
Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...
Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{ini
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Inkling Small is open-weight and competitive on many tasks, but Morph V3 Large may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Yes. Inkling Small has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Morph V3 Large is API-only and cannot be self-hosted.
Inkling Small costs $1.2/M output vs $1.9/M for Morph V3 Large — roughly 2x cheaper via API, and free if you self-host.
Choose Inkling Small 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 Morph V3 Large if you want frontier capability through a managed API with zero infrastructure to run.
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