Pick Mistral Medium 3 for strong cost-to-capability at $0.40/$2.00 or general reasoning, coding and multimodal tasks. Pick Qwen 3.8-Max for near-frontier quality at value pricing — artificial analysis intelligence index 58 or large 1m-token context with multimodal input (text, image, video). On a tight budget at scale, Mistral Medium 3 is the value pick.
Mistral Medium 3 (Mistral AI, France) and Qwen 3.8-Max (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Mistral Medium 3 is mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Qwen 3.8-Max is alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Mistral Medium 3 is about 5× cheaper on input ($0.4/$2 per 1M tokens vs $2/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Qwen 3.8-Max holds 8.2× more — 1M (~1,573 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Qwen 3.8-Max is the newer model by about 15 months (released August 3, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a France-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Mistral Medium 3
Qwen 3.8-Max
Provider
Mistral AI (France)
Alibaba (China)
Released
May 7, 2025
August 3, 2026
Context window
128K (~192 pages)
1M (~1,573 pages)
Price (in/out)
$0.4/$2 per 1M tokens
$2/$6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Strong cost-to-capability at $0.40/$2.00: Mistral Medium 3 — At $0.4/$2 per 1M tokens it undercuts Qwen 3.8-Max ($2/$6 per 1M tokens), and that gap compounds at volume.
General reasoning, coding and multimodal tasks: Mistral Medium 3 — Qwen 3.8-Max is comparatively weak here — flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced
Efficient mid-tier deployment for production workloads: Mistral Medium 3 — Mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier — and it runs cheaper at $0.4/$2 per 1M tokens.
Near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58: Qwen 3.8-Max — Mistral Medium 3 is comparatively weak here — a mid-tier model, not a frontier reasoner
Large 1M-token context with multimodal input (text, image, video): Qwen 3.8-Max — Its 1M window holds about 8.2× more than Mistral Medium 3's 128K in a single prompt.
Mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token: Qwen 3.8-Max — Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped — and it carries the larger 1M context.
Lowest cost at scale: Mistral Medium 3 — At $0.4/$2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Qwen 3.8-Max — Its 1M window is about 8.2× larger than Mistral Medium 3's 128K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Mistral Medium 3 — At $0.4/$2 per 1M tokens it undercuts Qwen 3.8-Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen 3.8-Max — Larger 1M window fits more in one prompt.
Anyone whose priority is strong cost-to-capability at $0.40/$2.00: Mistral Medium 3 — It is specifically built for that.
Anyone whose priority is near-frontier quality at value pricing — artificial analysis intelligence index 58: Qwen 3.8-Max — That is its strongest area.
An enterprise with regional data-residency rules: Qwen 3.8-Max or Mistral Medium 3 — Origin (France vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Mistral Medium 3: where it fits
Mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Released May 7, 2025 by Mistral AI, it is built for strong cost-to-capability at $0.40/$2.00, general reasoning, coding and multimodal tasks, efficient mid-tier deployment for production workloads, and text and image input.
Its trade-offs are real: a 128K context — smaller than the 1M-window flagships here, no published SWE-Bench Verified score, a mid-tier model, not a frontier reasoner, and proprietary, unlike Mistral's open-weight releases. At $0.4 in / $2 out per million tokens, it sits in the budget price band.
Qwen 3.8-Max: where it fits
Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. Released August 3, 2026 by Alibaba, it is built for near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58, large 1M-token context with multimodal input (text, image, video), mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token, and $2/$6 per million tokens — far below US flagships like Claude Opus 5 and GPT-5.6 Sol.
Its trade-offs: open weights were announced for release but had not shipped as of mid-August 2026 — a closed API for now, active-parameter count is not officially disclosed by Alibaba, flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced, and trails the very top models (Opus 5, Fable 5, GPT-5.6 Sol) on independent tests. At $2 in / $6 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Mistral Medium 3 (France) and Qwen 3.8-Max (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Medium 3 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is Mistral Medium 3 or Qwen 3.8-Max better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Mistral Medium 3 leans toward strong cost-to-capability at $0.40/$2.00 while Qwen 3.8-Max leans toward near-frontier quality at value pricing — artificial analysis intelligence index 58, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Mistral Medium 3 or Qwen 3.8-Max?
Mistral Medium 3 is cheaper — $0.4/$2 per 1M tokens vs $2/$6 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
Qwen 3.8-Max — 1M vs 128K, about 8.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Mistral Medium 3 and Qwen 3.8-Max together?
Yes — a multi-model platform like LumiChats gives you Mistral Medium 3, Qwen 3.8-Max and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Mistral Medium 3 or Qwen 3.8-Max?
Qwen 3.8-Max — released August 3, 2026, about 15 months after Mistral Medium 3.
Mistral Medium 3 vs Qwen 3.8-Max
Mistral AI · France | Alibaba · China · Updated June 2026
Quick verdict
Pick Mistral Medium 3 for strong cost-to-capability at $0.40/$2.00 or general reasoning, coding and multimodal tasks. Pick Qwen 3.8-Max for near-frontier quality at value pricing — artificial analysis intelligence index 58 or large 1m-token context with multimodal input (text, image, video). On a tight budget at scale, Mistral Medium 3 is the value pick.
Mistral Medium 3 (Mistral AI, France) and Qwen 3.8-Max (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Mistral Medium 3 is mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Qwen 3.8-Max is alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Mistral Medium 3 is about 5× cheaper on input ($0.4/$2 per 1M tokens vs $2/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Qwen 3.8-Max holds 8.2× more — 1M (~1,573 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Qwen 3.8-Max is the newer model by about 15 months (released August 3, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a France-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Mistral Medium 3
Qwen 3.8-Max
Provider
Mistral AI (France)
Alibaba (China)
Released
May 7, 2025
August 3, 2026
Context window
128K (~192 pages)
1M (~1,573 pages)
Price (in/out)
$0.4/$2 per 1M tokens
$2/$6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Strong cost-to-capability at $0.40/$2.00
Mistral Medium 3
At $0.4/$2 per 1M tokens it undercuts Qwen 3.8-Max ($2/$6 per 1M tokens), and that gap compounds at volume.
General reasoning, coding and multimodal tasks
Mistral Medium 3
Qwen 3.8-Max is comparatively weak here — flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced
Efficient mid-tier deployment for production workloads
Mistral Medium 3
Mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier — and it runs cheaper at $0.4/$2 per 1M tokens.
Near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58
Qwen 3.8-Max
Mistral Medium 3 is comparatively weak here — a mid-tier model, not a frontier reasoner
Large 1M-token context with multimodal input (text, image, video)
Qwen 3.8-Max
Its 1M window holds about 8.2× more than Mistral Medium 3's 128K in a single prompt.
Mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token
Qwen 3.8-Max
Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped — and it carries the larger 1M context.
Lowest cost at scale
Mistral Medium 3
At $0.4/$2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Qwen 3.8-Max
Its 1M window is about 8.2× larger than Mistral Medium 3's 128K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral Medium 3
At $0.4/$2 per 1M tokens it undercuts Qwen 3.8-Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen 3.8-Max
Larger 1M window fits more in one prompt.
Anyone whose priority is strong cost-to-capability at $0.40/$2.00
→ Mistral Medium 3
It is specifically built for that.
Anyone whose priority is near-frontier quality at value pricing — artificial analysis intelligence index 58
→ Qwen 3.8-Max
That is its strongest area.
An enterprise with regional data-residency rules
→ Qwen 3.8-Max or Mistral Medium 3
Origin (France vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Mistral Medium 3: where it fits
Mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Released May 7, 2025 by Mistral AI, it is built for strong cost-to-capability at $0.40/$2.00, general reasoning, coding and multimodal tasks, efficient mid-tier deployment for production workloads, and text and image input.
Its trade-offs are real: a 128K context — smaller than the 1M-window flagships here, no published SWE-Bench Verified score, a mid-tier model, not a frontier reasoner, and proprietary, unlike Mistral's open-weight releases. At $0.4 in / $2 out per million tokens, it sits in the budget price band.
Qwen 3.8-Max: where it fits
Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. Released August 3, 2026 by Alibaba, it is built for near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58, large 1M-token context with multimodal input (text, image, video), mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token, and $2/$6 per million tokens — far below US flagships like Claude Opus 5 and GPT-5.6 Sol.
Its trade-offs: open weights were announced for release but had not shipped as of mid-August 2026 — a closed API for now, active-parameter count is not officially disclosed by Alibaba, flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced, and trails the very top models (Opus 5, Fable 5, GPT-5.6 Sol) on independent tests. At $2 in / $6 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Mistral Medium 3 (France) and Qwen 3.8-Max (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Medium 3 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Mistral Medium 3 and Qwen 3.8-Max without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is Mistral Medium 3 or Qwen 3.8-Max better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Mistral Medium 3 leans toward strong cost-to-capability at $0.40/$2.00 while Qwen 3.8-Max leans toward near-frontier quality at value pricing — artificial analysis intelligence index 58, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Mistral Medium 3 or Qwen 3.8-Max?
Mistral Medium 3 is cheaper — $0.4/$2 per 1M tokens vs $2/$6 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
Qwen 3.8-Max — 1M vs 128K, about 8.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Mistral Medium 3 and Qwen 3.8-Max together?
Yes — a multi-model platform like LumiChats gives you Mistral Medium 3, Qwen 3.8-Max and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Mistral Medium 3 or Qwen 3.8-Max?
Qwen 3.8-Max — released August 3, 2026, about 15 months after Mistral Medium 3.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.