Qwen3.8 27B vs Mistral Large 2.1 vs Inkling Small
Too close to call on our weighted score (Inkling Small 68, Qwen3.8 27B 66, Mistral Large 2.1 38). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.8 27B
66/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
Mistral AIMistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
Thinking MachinesInkling Small
68/100- ECI150.5
- Price$0.58 / $1.44
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 2 points (Inkling Small 68/100, Qwen3.8 27B 66/100, Mistral Large 2.1 38/100), so choose by what matters most for your work: Qwen3.8 27B for raw capability, Inkling Small on price and Inkling Small for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.8 27BCapabilities Index (ECI): Qwen3.8 27B 151.8 · Inkling Small 150.5 · Mistral Large 2.1 128.6
- Lowest priceInkling SmallInkling Small $0.795 · Qwen3.8 27B $0.938 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · Qwen3.8 27B 262,144 · Mistral Large 2.1 131,072 tokens
- Widest inputsQwen3.8 27B and Inkling SmallQwen3.8 27B: Text, Images, Video · Mistral Large 2.1: Text · 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 2.1 | Inkling Small |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 79 | 50 | 77 |
| Price | 25% | 49 | 26 | 52 |
| Inputs & features | 15% | 70 | 25 | 70 |
| Context window | 10% | 37 | 25 | 61 |
| Overall | 100% | 66/100 | 38/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.8 27BAlibaba (Qwen) | Mistral Large 2.1Mistral AI | Inkling SmallThinking Machines |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 151.8 (best) | 128.6 | 150.5 |
| ECI rank | #54 of 165 (best) | #142 of 165 | #58 of 165 |
| GPQA DiamondGraduate-level science questions | — | 51.3% | 88.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 46.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% | 90.0% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 19.1% |
| Price per million tokens | |||
| Input | $0.40 (best) | $2.00 | $0.58 |
| Output | $2.55 | $6.00 | $1.44 (best) |
| Cached input | — | — | $0.116 |
| Blended (3:1) | $0.938 | $3.00 | $0.795 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 49 providers | Median of 2 providers | Official Thinking Machines API |
| Limits | |||
| Context window | 262,144 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 16,384 tokens | 1,048,576 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | 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 | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache-2.0 |
| API model ID | — | — | thinkingmachines/Inkling-Small |
| API providers | 53 (best) | 2 | 12 |
| Released | Aug 14, 2026 | Nov 18, 2024 | 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 2.1$32.00
- Inkling Small$8.68
Which should you choose?
Which is better: Qwen3.8 27B, Mistral Large 2.1 or Inkling Small?
It is close. Our weighted score puts them within 2 points (Inkling Small 68/100, Qwen3.8 27B 66/100, Mistral Large 2.1 38/100), so choose by what matters most for your work: Qwen3.8 27B for raw capability, Inkling Small on price and Inkling Small for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.8 27B, Mistral Large 2.1 or Inkling Small?
Inkling Small is cheaper at $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); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.795 per million tokens for Inkling Small versus $0.938 for Qwen3.8 27B (1.2× as much) and $3.00 for Mistral Large 2.1 (3.8× as much).
Which scores higher on benchmarks?
Qwen3.8 27B scores higher on the Capabilities Index (ECI): Qwen3.8 27B 151.8 (#54 of 165), Inkling Small 150.5 (#58 of 165) and Mistral Large 2.1 128.6 (#142 of 165). The confidence ranges of the top two overlap (149.9–153.6 vs 147.7–152.4), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.8 27B, Mistral Large 2.1 and Inkling Small yet, so there is no like-for-like coding score. On overall capability, Qwen3.8 27B leads, which tends to carry over to coding, but test on your own codebase. 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 131,072 for Mistral Large 2.1. Maximum output per response: Qwen3.8 27B up to 32,768, Mistral Large 2.1 up to 16,384, 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 2.1 accepts text; 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 2.1 came out Nov 18, 2024. Knowledge cutoff: Mistral Large 2.1 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.
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