Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). 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; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI, 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. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. 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: GPT-4o mini is about 10× cheaper on input ($0.15/$0.6 per 1M tokens vs $1.5/$7.5 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Mistral Medium 3.5 holds 2× more — 256K (~384 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: Mistral Medium 3.5 is the newer model by about 22 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
GPT-4o mini
Mistral Medium 3.5
Provider
OpenAI (US)
Mistral AI (France)
Released
July 18, 2024
April 29, 2026
Context window
128K (~192 pages)
256K (~384 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$1.5/$7.5 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very low cost per token for its capability tier: GPT-4o mini — At $0.15/$0.6 per 1M tokens it undercuts Mistral Medium 3.5 ($1.5/$7.5 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval): GPT-4o mini — OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch — and it runs cheaper at $0.15/$0.6 per 1M tokens.
Leading MMLU among peer small models (82%): GPT-4o mini — GPT-4o mini lists leading MMLU among peer small models (82%) among its strengths; Mistral Medium 3.5 does not.
Strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier: 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 carries the larger 256K context.
128B dense open-weight model — self-hostable: Mistral Medium 3.5 — Open weights make this possible at all — GPT-4o mini 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 — GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
Lowest cost at scale: GPT-4o mini — At $0.15/$0.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Mistral Medium 3.5 — Its 256K window is about 2× larger than GPT-4o mini's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-4o mini — At $0.15/$0.6 per 1M tokens it undercuts Mistral Medium 3.5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Mistral Medium 3.5 — Larger 256K 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; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier: GPT-4o mini — 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: GPT-4o mini 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.
GPT-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 out per million tokens, it sits in the budget 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. GPT-4o mini 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 GPT-4o mini 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, GPT-4o mini leans toward very low cost per token for its capability tier 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, GPT-4o mini 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 GPT-4o mini is API-metered at $0.15/$0.6 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?
Mistral Medium 3.5 — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-4o mini and Mistral Medium 3.5 together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, 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, GPT-4o mini or Mistral Medium 3.5?
Mistral Medium 3.5 — released April 29, 2026, about 22 months after GPT-4o mini.
GPT-4o mini vs Mistral Medium 3.5
OpenAI · US | Mistral AI · France · Updated June 2026
Quick verdict
Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). 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; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI, 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. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. 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: GPT-4o mini is about 10× cheaper on input ($0.15/$0.6 per 1M tokens vs $1.5/$7.5 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Mistral Medium 3.5 holds 2× more — 256K (~384 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: Mistral Medium 3.5 is the newer model by about 22 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
GPT-4o mini
Mistral Medium 3.5
Provider
OpenAI (US)
Mistral AI (France)
Released
July 18, 2024
April 29, 2026
Context window
128K (~192 pages)
256K (~384 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$1.5/$7.5 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very low cost per token for its capability tier
GPT-4o mini
At $0.15/$0.6 per 1M tokens it undercuts Mistral Medium 3.5 ($1.5/$7.5 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval)
GPT-4o mini
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch — and it runs cheaper at $0.15/$0.6 per 1M tokens.
Leading MMLU among peer small models (82%)
GPT-4o mini
GPT-4o mini lists leading MMLU among peer small models (82%) among its strengths; Mistral Medium 3.5 does not.
Strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier
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 carries the larger 256K context.
128B dense open-weight model — self-hostable
Mistral Medium 3.5
Open weights make this possible at all — GPT-4o mini 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
GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
Lowest cost at scale
GPT-4o mini
At $0.15/$0.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Mistral Medium 3.5
Its 256K window is about 2× larger than GPT-4o mini's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-4o mini
At $0.15/$0.6 per 1M tokens it undercuts Mistral Medium 3.5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Mistral Medium 3.5
Larger 256K 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; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier
→ GPT-4o mini
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
→ GPT-4o mini 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.
GPT-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 out per million tokens, it sits in the budget 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. GPT-4o mini 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 GPT-4o mini 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 GPT-4o mini 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, GPT-4o mini leans toward very low cost per token for its capability tier 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, GPT-4o mini 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 GPT-4o mini is API-metered at $0.15/$0.6 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?
Mistral Medium 3.5 — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-4o mini and Mistral Medium 3.5 together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, 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, GPT-4o mini or Mistral Medium 3.5?
Mistral Medium 3.5 — released April 29, 2026, about 22 months after GPT-4o mini.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.