Is Ministral 3 14B 2512 better than Qwen3.6 35B A3B?

On accuracy they are statistically tied: Ministral 3 14B 2512 scored 33.8% and Qwen3.6 35B A3B 37.5% on the same 80 questions from open datasets. Similar accuracy; Ministral 3 14B 2512 costs less per question.

Which is cheaper, Ministral 3 14B 2512 or Qwen3.6 35B A3B?

Ministral 3 14B 2512 is cheaper: $0.00031 per question as billed, against $0.0037. List prices: Ministral 3 14B 2512 $0.2 in / $0.2 out, Qwen3.6 35B A3B $0.1 in / $0.95 out per million tokens.

Which is faster, Ministral 3 14B 2512 or Qwen3.6 35B A3B?

Qwen3.6 35B A3B is faster at 135 output tokens per second, against 32. Time to first token: 0.6 s for Ministral 3 14B 2512, 0.4 s for Qwen3.6 35B A3B.

Which has the larger context window, Ministral 3 14B 2512 or Qwen3.6 35B A3B?

Both accept about the same amount of context: 262k and 262k tokens.

Head to head · tested 27 Oct 2026

Ministral 3 14B 2512 vs Qwen3.6 35B A3B: accuracy, speed and price

Similar accuracy; Ministral 3 14B 2512 costs less per question.

MistralMinistral 3 14B 251233.8% score · #61$0.00031 per question32 tok/s

QwenQwen3.6 35B A3B37.5% score · #59$0.0037 per question135 tok/s

Verdict

Built from the measurements below

Accuracy is too close to call: Ministral 3 14B 2512 scored 33.8% and Qwen3.6 35B A3B 37.5% on the same 80 questions, a gap within what chance alone could produce.
Ministral 3 14B 2512 is 12× cheaper per question as billed ($0.00031 vs $0.0037), reasoning tokens included.
Qwen3.6 35B A3B generates text 4.2× faster: 135 tokens per second against 32.
Qwen3.6 35B A3B starts answering sooner: 0.4 s to the first token, against 0.6 s for Ministral 3 14B 2512.

Which should you choose?

Choose Ministral 3 14B 2512 for the lower cost per correct answer ($0.00092 against $0.0099) and shorter outputs (about 1,255 tokens per question against 3,563). Choose Qwen3.6 35B A3B for faster streaming, a quicker first token and more hosts to choose from on OpenRouter (10 against 1). The widest subject gap is in humanities & social science: Qwen3.6 35B A3B answered 7 of 9 correctly, Ministral 3 14B 2512 5 of 9. With so few questions per subject, treat this as a lead to test on your own prompts, not a result.

Accuracy gap -3.7 points (z = -0.50). A winner is called only when z is at least 1.96, the usual 95% threshold. Speed, latency and price need a 10% margin.

Head to head

Same questions, same prompt, same API

Measure	Ministral 3 14B 2512	Qwen3.6 35B A3B	Edge
Verification scoreHigher is better	33.8%	37.5%	Too close to call
Cost per questionLower is better	$0.00031	$0.0037	Ministral 3 14B 2512
Output speedHigher is better	32	135	Qwen3.6 35B A3B
Time to first tokenLower is better	0.6 s	0.4 s	Qwen3.6 35B A3B
End-to-end response timeLower is better	12.7 s	5.6 s	Qwen3.6 35B A3B
Tokens per questionLower is better	1,255	3,563	Ministral 3 14B 2512
Context windowHigher is better	262k	262k	Similar

Side by side

Bars are scaled to the larger of the two values. The winner on each measure is marked in bold.

Every measure

Ministral 3 14B 2512 first, Qwen3.6 35B A3B second

Verification scorehigher is better

Ministral 3 14B 251233.8%

Qwen3.6 35B A3B37.5%

Cost per questionlower is better

Ministral 3 14B 2512$0.00031

Qwen3.6 35B A3B$0.0037

Output speedhigher is better

Ministral 3 14B 251232

Qwen3.6 35B A3B135

Time to first tokenlower is better

Ministral 3 14B 25120.6 s

Qwen3.6 35B A3B0.4 s

End-to-end response timelower is better

Ministral 3 14B 251212.7 s

Qwen3.6 35B A3B5.6 s

Tokens per questionlower is better

Ministral 3 14B 25121,255

Qwen3.6 35B A3B3,563

Context windowhigher is better

Ministral 3 14B 2512262k

Qwen3.6 35B A3B262k

Score vs. cost per question

Both models in color, every other model in grey

Results by subject and dataset

Questions answered correctly. Small samples: a gap of one or two questions is noise.

Subject	Questions	Ministral 3 14B 2512	Qwen3.6 35B A3B
Science	30	8/30	9/30
Engineering	15	2/15	2/15
Medicine	11	6/11	6/11
Economics	9	4/9	5/9
Law	6	2/6	1/6
Humanities & social science	9	5/9	7/9
SuperGPQA dataset	50	8/50	9/50
MMLU-Pro dataset	30	19/30	21/30

Specifications and pricing

From OpenRouter's catalogue, plus the leaderboard's test dates

Specification	Ministral 3 14B 2512	Qwen3.6 35B A3B
Creator	Mistral	Qwen
Released	2 Dec 2025	27 Apr 2026
Weights	Open weights	Open weights
Reasoning	No	Yes
Input	text, image	text, image, video
Output	text	text
Context window	262k tokens	262k tokens
Knowledge cutoff	—	—
List price in / out per 1M	$0.2 / $0.2	$0.1 / $0.95
Blended price per 1M (3:1)	$0.20	$0.31
Cheapest host	Mistral · $0.2 / $0.2	Darkbloom · $0.05 / $0.7
Hosts on OpenRouter	1	10
Last tested	26 Oct 2026	26 Oct 2026

Compare any two models →

Ministral 3 14B 2512 vs Qwen3.6 35B A3B: frequently asked questions

Is Ministral 3 14B 2512 better than Qwen3.6 35B A3B?: On accuracy they are statistically tied: Ministral 3 14B 2512 scored 33.8% and Qwen3.6 35B A3B 37.5% on the same 80 questions from open datasets. Similar accuracy; Ministral 3 14B 2512 costs less per question.
Which is cheaper, Ministral 3 14B 2512 or Qwen3.6 35B A3B?: Ministral 3 14B 2512 is cheaper: $0.00031 per question as billed, against $0.0037. List prices: Ministral 3 14B 2512 $0.2 in / $0.2 out, Qwen3.6 35B A3B $0.1 in / $0.95 out per million tokens.
Which is faster, Ministral 3 14B 2512 or Qwen3.6 35B A3B?: Qwen3.6 35B A3B is faster at 135 output tokens per second, against 32. Time to first token: 0.6 s for Ministral 3 14B 2512, 0.4 s for Qwen3.6 35B A3B.
Which has the larger context window, Ministral 3 14B 2512 or Qwen3.6 35B A3B?: Both accept about the same amount of context: 262k and 262k tokens.