Inkling Small vs GPT-6 Luna vs Qwen3.8 27B
GPT-6 Luna comes out ahead, 78 to 62 and 57 on our weighted score, and it is the cheaper option too.
Thinking MachinesInkling Small
62/100- ECI150.2
- Price$0.50 / $1.20
- Context1.05M
- Our pick
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
Alibaba (Qwen)Qwen3.8 27B
57/100- ECI149.4
- Price$0.40 / $2.50
- Context262K
GPT-6 Luna is our pick
GPT-6 Luna is the better all-round choice, scoring 78/100 against Inkling Small (62) and Qwen3.8 27B (57). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · Inkling Small $0.675 · Qwen3.8 27B $0.925 per 1M tokens (3:1 blend)
- Longest contextGPT-6 Luna and Inkling SmallGPT-6 Luna 1,050,000 · Inkling Small 1,048,576 · Qwen3.8 27B 262,144 tokens
- Widest inputsSame inputsInkling Small: Text, Images, Audio · GPT-6 Luna: Text, Images, PDFs · Qwen3.8 27B: Text, Images, Video
- Self-hostingInkling Small and Qwen3.8 27BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Inkling Small | GPT-6 Luna | Qwen3.8 27B |
|---|---|---|---|---|
| Price | 50% | 58 | 83 | 51 |
| Inputs & features | 30% | 70 | 80 | 80 |
| Context window | 20% | 61 | 61 | 37 |
| Overall | 100% | 62/100 | 78/100 | 57/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Inkling SmallThinking Machines | GPT-6 LunaOpenAI | Qwen3.8 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 150.2 (best) | — | 149.4 |
| ECI rank | #47 of 148 (best) | — | #53 of 148 |
| GPQA DiamondGraduate-level science questions | 88.5% | 90.5% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 46.3% | 79.0% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 90.0% | 98.9% (best) | — |
| SimpleQA VerifiedShort factual questions | 19.1% | 41.4% (best) | — |
| Price per million tokens | |||
| Input | $0.50 | $0.10 (best) | $0.40 |
| Output | $1.20 | $0.50 (best) | $2.50 |
| Cached input | — | $0.01 | — |
| Blended (3:1) | $0.675 | $0.20 (best) | $0.925 |
| Long-context rate | Same rate | Over 272K: $0.20 / $0.75 | Same rate |
| Price source | Median of 11 providers | Official OpenAI API | Median of 39 providers |
| Limits | |||
| Context window | 1,048,576 tokens | 1,050,000 tokens (best) | 262,144 tokens |
| Max output | 1,048,576 tokens (best) | 128,000 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | Yes | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Open |
| API model ID | — | gpt-6-luna | — |
| API providers | 11 | 24 | 41 (best) |
| Released | Jul 30, 2026 | Sep 22, 2026 | Aug 14, 2026 |
| Knowledge cutoff | — | May 18, 2026 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Inkling Small$7.40
- GPT-6 Luna$2.00
- Qwen3.8 27B$9.00
Which should you choose?
Which is better: Inkling Small, GPT-6 Luna or Qwen3.8 27B?
GPT-6 Luna is the better all-round choice, scoring 78/100 against Inkling Small (62) and Qwen3.8 27B (57). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Inkling Small, GPT-6 Luna or Qwen3.8 27B?
GPT-6 Luna is cheaper at $0.10 input / $0.50 output per million tokens (official OpenAI API price). Inkling Small costs $0.50 input / $1.20 output per million tokens (median across 11 API providers); Qwen3.8 27B costs $0.40 input / $2.50 output per million tokens (median across 39 API providers). At a typical mix of three input tokens to one output token, that is $0.20 per million tokens for GPT-6 Luna versus $0.675 for Inkling Small (3.4× as much) and $0.925 for Qwen3.8 27B (4.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Inkling Small has an ECI of 150.2, GPT-6 Luna has not been scored yet and Qwen3.8 27B has an ECI of 149.4.
Which is better for coding?
There are no published SWE-bench Verified results for Inkling Small, GPT-6 Luna and Qwen3.8 27B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-6 Luna and Inkling Small have the largest context windows (1,050,000 and 1,048,576 tokens), against 262,144 for Qwen3.8 27B. Maximum output per response: Inkling Small up to 1,048,576, GPT-6 Luna up to 128,000, Qwen3.8 27B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Inkling Small accepts text, images and audio; GPT-6 Luna accepts text, images and PDFs; Qwen3.8 27B accepts text, images and video. They handle the same number of input types.
Are any of these open source?
Inkling Small and Qwen3.8 27B publishes its weights (Apache-2.0) and can be self-hosted; GPT-6 Luna is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. Qwen3.8 27B came out Aug 14, 2026; Inkling Small came out Jul 30, 2026. Knowledge cutoff: GPT-6 Luna May 18, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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