AI Models · Open-Source vs Paid

Qwen3.8 Max (0902) Open vs Mistral Large 3 2512 Paid

Qwen3.8 Max (0902) vs Mistral Large 3 2512 compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.

Mistral Large 3 2512 — full profile ›

Prices & specs refreshed from live data · olud.ai

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

Qwen3.8 Max (0902)OpenAlibaba
$2 /M input
$6 /M outputNo per-token fees if you self-host
TypeOpen-weight
Context window1M tokens
MultimodalYes
Self-hostYes
Mistral Large 3 2512PaidMistral AI
$0.5 /M input
$1.5 /M outputManaged API (no infra to run)
TypeProprietary
Context window262K tokens
MultimodalYes
Self-hostNo
Choose Qwen3.8 Max (0902) if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Mistral Large 3 2512 if you want frontier capability through a managed API with zero infrastructure to run.

Qwen3.8 Max (0902) vs Mistral Large 3 2512 specs

SpecQwen3.8 Max (0902)Mistral Large 3 2512Winner
MakerAlibabaMistral AI–
TypeOpen-weightProprietaryQwen3.8 Max (0902)
Context window1M tokens262K tokensQwen3.8 Max (0902)
Input price$2/M · free self-host$0.5/MMistral Large 3 2512
Output price$6/M · free self-host$1.5/MMistral Large 3 2512
Vision / multimodalYesYes= Tie
Tool / function callingYesYes= Tie
Self-hostableYesNo (API only)Qwen3.8 Max (0902)
LicenseApache 2.0ProprietaryQwen3.8 Max (0902)

Price gap & when to choose each

4.0×cheaper per output token

Mistral Large 3 2512 is ~4.0× cheaper than Qwen3.8 Max (0902) on output tokens ($1.5 vs $6 per M tokens).

Choose Qwen3.8 Max (0902) if…
  • You want to self-host or run on your cloud
  • You need the longer 1M context window
  • You prioritize data privacy & control
  • You are building open or reproducible AI
Choose Mistral Large 3 2512 if…
  • You want frontier performance through a managed API
  • You want the lower output price
  • You value reliability & ecosystem
  • You don't want to manage infrastructure

Feature comparison

CapabilityQwen3.8 Max (0902)Mistral Large 3 2512
Open weights (downloadable)✓✗
Self-hostable✓✗
Runs fully offline✓✗
Vision / multimodal✓✓
Tool / function calling✓✓
1M+ context window✓✗

Benchmarks: Qwen3.8 Max (0902) vs Mistral Large 3 2512

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

Qwen3.8 Max (0902)
Mistral Large 3 2512
Intelligence index
40.3
9.7
Coding index
71.8
20.1
GPQA
92.7%
68%
Humanity's Last Exam
43%
4.2%
Long Context Reasoning
78.3%
36%
SciCode
53.2%
36.6%
τ-Bench Banking
51.3%
5.8%
Terminal-Bench
81.3%
12%
Math index
–
38
MMLU-Pro
–
80.7%
LiveCodeBench
–
46.5%
AIME 2025
–
38%
IFBench
–
36.2%
τ²-Bench
–
24.6%
Terminal-Bench Hard
–
15.9%
Speed
41.4 tok/s
81.3 tok/s
Latency
1.8s
0.68s
Intelligence per $
13.4
12.9

Benchmark data by Artificial Analysis.

How Qwen3.8 Max (0902) and Mistral Large 3 2512 score

🏆 Best value & openness: Qwen3.8 Max (0902) (4.8 vs 3.2 / 5)
CriterionQwen3.8 Max (0902)Mistral Large 3 2512
Cost-efficiency4.04.5
Context window5.04.0
Openness5.01.5
Self-hosting5.01.0
Multimodality5.05.0

Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.

What each model is

Qwen3.8 Max (0902) Open

Alibaba · Open-weight

Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input and returns text,...

Mistral Large 3 2512 Paid

Mistral AI · Proprietary

Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.

Other models in these families

These variants are tracked but not compared here — one page per family keeps the comparison readable.

Other variants tracked
Qwen3.8 2.4T A95BQwen3.8 2.4T A95B (batch)Qwen3.8 27BQwen3.7 MaxQwen3.6 Max PreviewQwen3.6 PlusQwen3.7 PlusQwen3.5-27BQwen3.6 27BQwen3.5 397B A17BQwen3 Max ThinkingQwen3.5-35B-A3BQwen3.6 35B A3BQwen3.5-122B-A10BQwen3 MaxQwen3.5-9B (batch)Qwen3.5-9BQwen3 VL 235B A22B ThinkingQwen3 235B A22B Thinking 2507Qwen3 235B A22B Instruct 2507Qwen3 Coder 480B A35BQwen3 Next 80B A3B ThinkingQwen3 Coder NextQwen3 VL 235B A22B InstructQwen3 30B A3B Thinking 2507Qwen3 Next 80B A3B InstructQwen3 Coder 30B A3B InstructQwen3 VL 30B A3B ThinkingQwen3 235B A22BQwen3 VL 32B InstructQwen3 VL 8B ThinkingQwen3 VL 30B A3B InstructQwen2.5 72B InstructQwen3 30B A3BQwen3 30B A3B Instruct 2507Qwen3 VL 8B InstructQwen3 32BQwen2.5 Coder 32B InstructQwen3 14BQwen3 8BQwen3 Coder PlusQwen3.5 Plus 2026-04-20Qwen3.5 Plus 2026-02-15Qwen3.6 FlashQwen2.5 VL 72B InstructQwen3 Coder FlashQwen-PlusQwen Plus 0728Qwen3.8 FlashQwen3.5-FlashQwen2.5 7B InstructQwen3.7 Flash

Frequently asked questions

Is Qwen3.8 Max (0902) as good as Mistral Large 3 2512?

Qwen3.8 Max (0902) is open-weight and competitive on many tasks, but Mistral Large 3 2512 may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.

Can I run Qwen3.8 Max (0902) locally?

Yes. Qwen3.8 Max (0902) has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Mistral Large 3 2512 is API-only and cannot be self-hosted.

Qwen3.8 Max (0902) vs Mistral Large 3 2512 — which should I pick in 2026?

Choose Qwen3.8 Max (0902) if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Mistral Large 3 2512 if you want frontier capability through a managed API with zero infrastructure to run.

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