Qwen3.8 27B vs Mistral Large 3 vs Inkling Small
Inkling Small comes out ahead, 59 to 53 and 52 on our weighted score, though Mistral Large 3 is 6% cheaper per token.
Alibaba (Qwen)Qwen3.8 27B
53/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
- Our pick
Thinking MachinesInkling Small
59/100- ECI150.5
- Price$0.58 / $1.44
- Context1.05M
Inkling Small is our pick
Inkling Small is the better all-round choice, scoring 59/100 against Qwen3.8 27B (53) and Mistral Large 3 (52). It leads on context window. 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 priceMistral Large 3Mistral Large 3 $0.75 · Inkling Small $0.795 · Qwen3.8 27B $0.938 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · Qwen3.8 27B 262,144 · Mistral Large 3 262,144 tokens
- Widest inputsQwen3.8 27B and Inkling SmallQwen3.8 27B: Text, Images, Video · Mistral Large 3: Text, Images · Inkling Small: Text, Images, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.8 27B | Mistral Large 3 | Inkling Small |
|---|---|---|---|---|
| Price | 50% | 49 | 53 | 52 |
| Inputs & features | 30% | 70 | 60 | 70 |
| Context window | 20% | 37 | 37 | 61 |
| Overall | 100% | 53/100 | 52/100 | 59/100 |
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 | Qwen3.8 27BAlibaba (Qwen) | Mistral Large 3Mistral AI | Inkling SmallThinking Machines |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 151.8 (best) | — | 150.5 |
| ECI rank | #54 of 165 (best) | — | #58 of 165 |
| GPQA DiamondGraduate-level science questions | — | — | 88.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 46.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 90.0% |
| SimpleQA VerifiedShort factual questions | — | — | 19.1% |
| Price per million tokens | |||
| Input | $0.40 (best) | $0.50 | $0.58 |
| Output | $2.55 | $1.50 | $1.44 (best) |
| Cached input | — | $0.05 (best) | $0.116 |
| Blended (3:1) | $0.938 | $0.75 (best) | $0.795 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 49 providers | Official Mistral API | Official Thinking Machines API |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenApache-2.0 |
| API model ID | — | mistral-large-2512 | thinkingmachines/Inkling-Small |
| API providers | 53 (best) | 17 | 12 |
| Released | Aug 14, 2026 | Dec 2, 2025 | Jul 30, 2026 |
| Knowledge cutoff | — | Nov 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.8 27B$9.10
- Mistral Large 3$8.00
- Inkling Small$8.68
Which should you choose?
Which is better: Qwen3.8 27B, Mistral Large 3 or Inkling Small?
Inkling Small is the better all-round choice, scoring 59/100 against Qwen3.8 27B (53) and Mistral Large 3 (52). It leads on context window. 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, Qwen3.8 27B, Mistral Large 3 or Inkling Small?
Mistral Large 3 is cheaper at $0.50 input / $1.50 output per million tokens (official Mistral API price). Inkling Small costs $0.58 input / $1.44 output per million tokens (official Thinking Machines API price); Qwen3.8 27B costs $0.40 input / $2.55 output per million tokens (median across 49 API providers). At a typical mix of three input tokens to one output token, that is $0.75 per million tokens for Mistral Large 3 versus $0.795 for Inkling Small (1.1× as much) and $0.938 for Qwen3.8 27B (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.8 27B has an ECI of 151.8, Mistral Large 3 has not been scored yet and Inkling Small has an ECI of 150.5.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.8 27B, Mistral Large 3 and Inkling Small 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?
Inkling Small has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3.8 27B and 262,144 for Mistral Large 3. Maximum output per response: Qwen3.8 27B up to 32,768, Mistral Large 3 up to 262,144, Inkling Small up to 1,048,576 tokens.
Which can read images, PDFs, audio or video?
Qwen3.8 27B accepts text, images and video; Mistral Large 3 accepts text and images; Inkling Small accepts text, images and audio. Qwen3.8 27B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
Qwen3.8 27B is the newest, released Aug 14, 2026. Inkling Small came out Jul 30, 2026; Mistral Large 3 came out Dec 2, 2025. Knowledge cutoff: Mistral Large 3 Nov 2024.
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 Nov 2, 2026.
