Gemini 4 Argon vs Mistral Medium 3.5
Google · US | Mistral AI · France · Updated June 2026
Quick verdict
Pick Gemini 4 Argon for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses or google-reported deepswe v1.1 of 77.9% and lvbench long-video understanding of 91.7%. 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 4 Argon if you want a managed API.
Gemini 4 Argon (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 4 Argon is google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). 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/$10 per 1M tokens) — modest, but it adds up at steady volume.
- ▸Context window: Gemini 4 Argon holds 4.1× more — 1M tokens (~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: Gemini 4 Argon is the newer model by about 6 months (released October 13, 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 4 Argon | Mistral Medium 3.5 |
|---|---|---|
| Provider | Google (US) | Mistral AI (France) |
| Released | October 13, 2026 | April 28, 2026 |
| Context window | 1M tokens (~1,573 pages) | 256K (~384 pages) |
| Price (in/out) | $2/$10 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 | Not published | Not published |
Who wins what
1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses
Gemini 4 Argon
Its 1M tokens window holds about 4.1× more than Mistral Medium 3.5's 256K in a single prompt.
Google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%
Gemini 4 Argon
Mistral Medium 3.5 is comparatively weak here — no native video or audio
Artificial Analysis Intelligence Index of 53 at high effort (AI Weekly)
Gemini 4 Argon
Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1) — and it carries the larger 1M tokens 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 4 Argon ($2/$10 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 4 Argon 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 4 Argon
Its 1M tokens 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 4 Argon, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 4 Argon
Larger 1M tokens 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 4 Argon is API-only.
Anyone whose priority is 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses
→ Gemini 4 Argon
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 4 Argon 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 4 Argon: where it fits
Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Released October 13, 2026 by Google, it is built for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%, and artificial Analysis Intelligence Index of 53 at high effort (AI Weekly).
Its trade-offs are real: introductory $2/$10 pricing ends November 30; from December 1 it doubles to $4/$20, verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high, and announced September 30 for trusted cyber defenders only; broad paid-tier availability followed October 13 (not on the free tier). At $2 in / $10 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 28, 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 4 Argon 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 4 Argon 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.
See pricingFrequently asked questions
Is Gemini 4 Argon 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 4 Argon leans toward 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses 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 4 Argon 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 4 Argon is API-metered at $2/$10 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 4 Argon — 1M tokens 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 4 Argon and Mistral Medium 3.5 together?
Yes — a multi-model platform like LumiChats gives you Gemini 4 Argon, 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 4 Argon or Mistral Medium 3.5?
Gemini 4 Argon — released October 13, 2026, about 6 months after Mistral Medium 3.5.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.