THInkling Small Open vs 
GPT-4 Turbo Paid
Inkling Small vs GPT-4 Turbo compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$1.2 /M outputNo per-token fees if you self-host
$30 /M outputManaged API (no infra to run)
Inkling Small vs GPT-4 Turbo specs
| Spec | Inkling Small | GPT-4 Turbo | Winner |
|---|---|---|---|
| Maker | Thinkingmachines | OpenAI | – |
| Type | Open-weight | Proprietary | THInkling Small |
| Context window | 1M tokens | 128K tokens | THInkling Small |
| Input price | $0.45/M · free self-host | $10/M | THInkling Small |
| Output price | $1.2/M · free self-host | $30/M | THInkling Small |
| Vision / multimodal | Yes | Yes | = Tie |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | THInkling Small |
| License | Open weights | Proprietary | THInkling Small |
Price gap & when to choose each
Inkling Small is ~25× cheaper than GPT-4 Turbo on output tokens ($1.2 vs $30 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 want the lowest operating costs
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | Inkling Small | GPT-4 Turbo |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✓ | ✗ |
Benchmarks: Inkling Small vs GPT-4 Turbo
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How Inkling Small and GPT-4 Turbo score
| Criterion | Inkling Small | GPT-4 Turbo |
|---|---|---|
| Cost-efficiency | 4.5 | 3.0 |
| Context window | 5.0 | 3.5 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 5.0 |
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.
What each model is
Inkling Small Open
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...
GPT-4 Turbo Paid
The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Training data: up to December 2023.
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 Inkling Small as good as GPT-4 Turbo?
Inkling Small is open-weight and competitive on many tasks, but GPT-4 Turbo 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 Inkling Small locally?
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-4 Turbo is API-only and cannot be self-hosted.
How much cheaper is Inkling Small?
Inkling Small costs $1.2/M output vs $30/M for GPT-4 Turbo — roughly 25x cheaper via API, and free if you self-host.
Inkling Small vs GPT-4 Turbo — which should I pick in 2026?
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-4 Turbo if you want frontier capability through a managed API with zero infrastructure to run.
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