Inkling Small vs Mistral Large 2.1

Inkling Small is the better all-round choice, scoring 68/100 against Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Inkling Small is cheaper at $0.58 input / $1.44 output per million tokens (official Thinking Machines API price). 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 $3.00 for Mistral Large 2.1 (3.8× as much).

Inkling Small scores higher on the Capabilities Index (ECI): Inkling Small 150.5 (#58 of 165) and Mistral Large 2.1 128.6 (#142 of 165). Their confidence ranges do not overlap (147.7–152.4 vs 124.3–130.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — Inkling Small 88.5%, Mistral Large 2.1 51.3%; OTIS Mock AIME 2024–2025 — Inkling Small 90.0%, Mistral Large 2.1 7.8%.

There are no published SWE-bench Verified results for Inkling Small and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, Inkling Small leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Inkling Small has the largest context window at 1,048,576 tokens, against 131,072 for Mistral Large 2.1. Maximum output per response: Inkling Small up to 1,048,576, Mistral Large 2.1 up to 16,384 tokens.

Inkling Small accepts text, images and audio; Mistral Large 2.1 accepts text. Inkling Small handles the widest range of inputs.

Yes, both publish their weights (Apache-2.0), so you can self-host them.

Inkling Small is the newest, released Jul 30, 2026. Mistral Large 2.1 came out Nov 18, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024.

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.