AI Models · Open-Source vs Paid

Qwen3.8 Max (0902) Open vs Codestral 2508 (batch) Paid

Qwen3.8 Max (0902) vs Codestral 2508 (batch) compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.

Codestral 2508 (batch) — 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
Codestral 2508 (batch)PaidMistral AI
$0.15 /M input
$0.45 /M outputManaged API (no infra to run)
TypeProprietary
Context window256K tokens
MultimodalNo
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 Codestral 2508 (batch) if you want frontier capability through a managed API with zero infrastructure to run.

Qwen3.8 Max (0902) vs Codestral 2508 (batch) specs

SpecQwen3.8 Max (0902)Codestral 2508 (batch)Winner
MakerAlibabaMistral AI–
TypeOpen-weightProprietaryQwen3.8 Max (0902)
Context window1M tokens256K tokensQwen3.8 Max (0902)
Input price$2/M · free self-host$0.15/MCodestral 2508 (batch)
Output price$6/M · free self-host$0.45/MCodestral 2508 (batch)
Vision / multimodalYesNoQwen3.8 Max (0902)
Tool / function callingYesYes= Tie
Self-hostableYesNo (API only)Qwen3.8 Max (0902)
LicenseApache 2.0ProprietaryQwen3.8 Max (0902)

Price gap & when to choose each

13×cheaper per output token

Codestral 2508 (batch) is ~13× cheaper than Qwen3.8 Max (0902) on output tokens ($0.45 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 Codestral 2508 (batch) 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)Codestral 2508 (batch)
Open weights (downloadable)✓✗
Self-hostable✓✗
Runs fully offline✓✗
Vision / multimodal✓✗
Tool / function calling✓✓
1M+ context window✓✗

Benchmarks: Qwen3.8 Max (0902) vs Codestral 2508 (batch)

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

Qwen3.8 Max (0902)
Codestral 2508 (batch)
Intelligence index
40.3
–
Coding index
71.8
–
GPQA
92.7%
–
Humanity's Last Exam
43%
–
Long Context Reasoning
78.3%
–
SciCode
53.2%
–
τ-Bench Banking
51.3%
–
Terminal-Bench
81.3%
–
Speed
41.4 tok/s
–
Latency
1.8s
–
Intelligence per $
13.4
–

Benchmark data by Artificial Analysis.

How Qwen3.8 Max (0902) and Codestral 2508 (batch) score

🏆 Best value & openness: Qwen3.8 Max (0902) (4.8 vs 3.0 / 5)
CriterionQwen3.8 Max (0902)Codestral 2508 (batch)
Cost-efficiency4.05.0
Context window5.04.0
Openness5.01.5
Self-hosting5.01.0
Multimodality5.03.5

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,...

Codestral 2508 (batch) Paid

Mistral AI · Proprietary

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/ne

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 Codestral 2508 (batch)?

Qwen3.8 Max (0902) is open-weight and competitive on many tasks, but Codestral 2508 (batch) 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. Codestral 2508 (batch) is API-only and cannot be self-hosted.

Qwen3.8 Max (0902) vs Codestral 2508 (batch) — 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 Codestral 2508 (batch) if you want frontier capability through a managed API with zero infrastructure to run.

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