Inkling Small vs GPT-3.5 Turbo 16k compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
GPT-3.5 Turbo 16k — 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 | GPT-3.5 Turbo 16k | Winner |
|---|---|---|---|
| Maker | Thinkingmachines | OpenAI | – |
| Type | Open-weight | Proprietary | THInkling Small |
| Context window | 1M tokens | 16K tokens | THInkling Small |
| Input price | $0.45/M · free self-host | $3/M | THInkling Small |
| Output price | $1.2/M · free self-host | $4/M | THInkling Small |
| Vision / multimodal | Yes | No | THInkling Small |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | THInkling Small |
| License | Open weights | Proprietary | THInkling Small |
Inkling Small is ~3.3× cheaper than GPT-3.5 Turbo 16k on output tokens ($1.2 vs $4 per M tokens).
| Capability | Inkling Small | GPT-3.5 Turbo 16k |
|---|---|---|
| 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 | GPT-3.5 Turbo 16k |
|---|---|---|
| Cost-efficiency | 4.5 | 4.0 |
| Context window | 5.0 | 2.0 |
| 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.
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...
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...
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 GPT-3.5 Turbo 16k 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. GPT-3.5 Turbo 16k is API-only and cannot be self-hosted.
Inkling Small costs $1.2/M output vs $4/M for GPT-3.5 Turbo 16k — roughly 3x 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 GPT-3.5 Turbo 16k if you want frontier capability through a managed API with zero infrastructure to run.
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