Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. Pick Mistral Medium 3.5 for strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier or 128b dense open-weight model — self-hostable. Choose Mistral Medium 3.5 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, US) and Mistral Medium 3.5 (Mistral AI, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemini 3.1 Pro is a 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Mistral Medium 3.5 is mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Mistral Medium 3.5 is about 1.3× cheaper on input ($1.5/$7.5 per 1M tokens vs $2/$12 per 1M tokens) — modest, but it adds up at steady volume.
Context window: Gemini 3.1 Pro holds 4.1× more — 1M (~1,573 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Mistral Medium 3.5 is the newer model by about 2 months (released April 29, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Gemini 3.1 Pro
Mistral Medium 3.5
Provider
Google (US)
Mistral AI (France)
Released
February 19, 2026
April 29, 2026
Context window
1M (~1,573 pages)
256K (~384 pages)
Price (in/out)
$2/$12 per 1M tokens
$1.5/$7.5 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window: Gemini 3.1 Pro — Its 1M window holds about 4.1× more than Mistral Medium 3.5's 256K in a single prompt.
Long video and document analysis: Gemini 3.1 Pro — Mistral Medium 3.5 is comparatively weak here — no native video or audio
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it carries the larger 1M context.
Strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier: Mistral Medium 3.5 — At $1.5/$7.5 per 1M tokens it undercuts Gemini 3.1 Pro ($2/$12 per 1M tokens), and that gap compounds at volume.
128B dense open-weight model — self-hostable: Mistral Medium 3.5 — Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
Unifies reasoning and coding into one model with an adjustable reasoning effort: Mistral Medium 3.5 — Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30) — and it runs cheaper at $1.5/$7.5 per 1M tokens.
Lowest cost at scale: Mistral Medium 3.5 — At $1.5/$7.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Gemini 3.1 Pro — Its 1M window is about 4.1× larger than Mistral Medium 3.5's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Mistral Medium 3.5 — At $1.5/$7.5 per 1M tokens it undercuts Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 3.1 Pro — Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: Mistral Medium 3.5 — Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window: Gemini 3.1 Pro — It is specifically built for that.
Anyone whose priority is strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier: Mistral Medium 3.5 — That is its strongest area.
An enterprise with regional data-residency rules: Gemini 3.1 Pro or Mistral Medium 3.5 — Origin (US vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Gemini 3.1 Pro: where it fits
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid price band.
Mistral Medium 3.5: where it fits
Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). Released April 29, 2026 by Mistral AI, it is built for strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier, 128B dense open-weight model — self-hostable, unifies reasoning and coding into one model with an adjustable reasoning effort, and 256K context with text and image input.
Its trade-offs: below the absolute frontier — a value/efficiency pick, not a flagship-beater, output pricing ($7.50/M) is higher than the cheapest Chinese rivals, license framing ('open' vs enterprise) varies across sources — confirm terms for commercial use, and no native video or audio. At $1.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. Mistral Medium 3.5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Gemini 3.1 Pro or Mistral Medium 3.5 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, Gemini 3.1 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window while Mistral Medium 3.5 leans toward strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or Mistral Medium 3.5?
Mistral Medium 3.5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 3.1 Pro — 1M vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and Mistral Medium 3.5 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, Mistral Medium 3.5 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, Gemini 3.1 Pro or Mistral Medium 3.5?
Mistral Medium 3.5 — released April 29, 2026, about 2 months after Gemini 3.1 Pro.
Gemini 3.1 Pro vs Mistral Medium 3.5
Google · US | Mistral AI · France · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. Pick Mistral Medium 3.5 for strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier or 128b dense open-weight model — self-hostable. Choose Mistral Medium 3.5 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, US) and Mistral Medium 3.5 (Mistral AI, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemini 3.1 Pro is a 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Mistral Medium 3.5 is mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Mistral Medium 3.5 is about 1.3× cheaper on input ($1.5/$7.5 per 1M tokens vs $2/$12 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: Gemini 3.1 Pro holds 4.1× more — 1M (~1,573 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Mistral Medium 3.5 is the newer model by about 2 months (released April 29, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Gemini 3.1 Pro
Mistral Medium 3.5
Provider
Google (US)
Mistral AI (France)
Released
February 19, 2026
April 29, 2026
Context window
1M (~1,573 pages)
256K (~384 pages)
Price (in/out)
$2/$12 per 1M tokens
$1.5/$7.5 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window
Gemini 3.1 Pro
Its 1M window holds about 4.1× more than Mistral Medium 3.5's 256K in a single prompt.
Long video and document analysis
Gemini 3.1 Pro
Mistral Medium 3.5 is comparatively weak here — no native video or audio
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it carries the larger 1M context.
Strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier
Mistral Medium 3.5
At $1.5/$7.5 per 1M tokens it undercuts Gemini 3.1 Pro ($2/$12 per 1M tokens), and that gap compounds at volume.
128B dense open-weight model — self-hostable
Mistral Medium 3.5
Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
Unifies reasoning and coding into one model with an adjustable reasoning effort
Mistral Medium 3.5
Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30) — and it runs cheaper at $1.5/$7.5 per 1M tokens.
Lowest cost at scale
Mistral Medium 3.5
At $1.5/$7.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Gemini 3.1 Pro
Its 1M window is about 4.1× larger than Mistral Medium 3.5's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral Medium 3.5
At $1.5/$7.5 per 1M tokens it undercuts Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 3.1 Pro
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Mistral Medium 3.5
Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window
→ Gemini 3.1 Pro
It is specifically built for that.
Anyone whose priority is strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier
→ Mistral Medium 3.5
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemini 3.1 Pro or Mistral Medium 3.5
Origin (US vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Gemini 3.1 Pro: where it fits
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid price band.
Mistral Medium 3.5: where it fits
Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). Released April 29, 2026 by Mistral AI, it is built for strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier, 128B dense open-weight model — self-hostable, unifies reasoning and coding into one model with an adjustable reasoning effort, and 256K context with text and image input.
Its trade-offs: below the absolute frontier — a value/efficiency pick, not a flagship-beater, output pricing ($7.50/M) is higher than the cheapest Chinese rivals, license framing ('open' vs enterprise) varies across sources — confirm terms for commercial use, and no native video or audio. At $1.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. Mistral Medium 3.5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Gemini 3.1 Pro and Mistral Medium 3.5 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 Gemini 3.1 Pro or Mistral Medium 3.5 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, Gemini 3.1 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window while Mistral Medium 3.5 leans toward strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or Mistral Medium 3.5?
Mistral Medium 3.5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 3.1 Pro — 1M vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and Mistral Medium 3.5 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, Mistral Medium 3.5 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, Gemini 3.1 Pro or Mistral Medium 3.5?
Mistral Medium 3.5 — released April 29, 2026, about 2 months after Gemini 3.1 Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.