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 for strong cost-to-capability at $0.40/$2.00 or general reasoning, coding and multimodal tasks. On a tight budget at scale, GPT-4o mini is the value pick.
GPT-4o mini (OpenAI, US) and Mistral Medium 3 (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 is mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Price: GPT-4o mini is about 2.7× cheaper on input ($0.15/$0.6 per 1M tokens vs $0.4/$2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: both advertise 128K (~192 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Mistral Medium 3 is the newer model by about 10 months (released May 7, 2025), 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
Provider
OpenAI (US)
Mistral AI (France)
Released
July 18, 2024
May 7, 2025
Context window
128K (~192 pages)
128K (~192 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$0.4/$2 per 1M tokens
Open weight?
No — API only
No — API only
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 ($0.4/$2 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval): GPT-4o mini — Mistral Medium 3 is comparatively weak here — a 128K context — smaller than the 1M-window flagships here
Leading MMLU among peer small models (82%): 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.
Strong cost-to-capability at $0.40/$2.00: 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 is the newer of the two.
General reasoning, coding and multimodal tasks: Mistral Medium 3 — GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
Efficient mid-tier deployment for production workloads: Mistral Medium 3 — Mistral Medium 3 lists efficient mid-tier deployment for production workloads among its strengths; GPT-4o mini does not.
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.
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, and on millions of tokens that margin decides the monthly bill.
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 cost-to-capability at $0.40/$2.00: Mistral Medium 3 — That is its strongest area.
An enterprise with regional data-residency rules: GPT-4o mini or Mistral Medium 3 — 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: 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: 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.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GPT-4o mini (US) and Mistral Medium 3 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. GPT-4o mini 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 GPT-4o mini or Mistral Medium 3 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 leans toward strong cost-to-capability at $0.40/$2.00, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or Mistral Medium 3?
GPT-4o mini is cheaper — $0.15/$0.6 per 1M tokens vs $0.4/$2 per 1M tokens, roughly 2.7× apart on input.
Which has the bigger context window?
Both advertise 128K (~192 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-4o mini and Mistral Medium 3 together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, Mistral Medium 3 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?
Mistral Medium 3 — released May 7, 2025, about 10 months after GPT-4o mini.
GPT-4o mini vs Mistral Medium 3
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 for strong cost-to-capability at $0.40/$2.00 or general reasoning, coding and multimodal tasks. On a tight budget at scale, GPT-4o mini is the value pick.
GPT-4o mini (OpenAI, US) and Mistral Medium 3 (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 is mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Price: GPT-4o mini is about 2.7× cheaper on input ($0.15/$0.6 per 1M tokens vs $0.4/$2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: both advertise 128K (~192 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Mistral Medium 3 is the newer model by about 10 months (released May 7, 2025), 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
Provider
OpenAI (US)
Mistral AI (France)
Released
July 18, 2024
May 7, 2025
Context window
128K (~192 pages)
128K (~192 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$0.4/$2 per 1M tokens
Open weight?
No — API only
No — API only
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 ($0.4/$2 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval)
GPT-4o mini
Mistral Medium 3 is comparatively weak here — a 128K context — smaller than the 1M-window flagships here
Leading MMLU among peer small models (82%)
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.
Strong cost-to-capability at $0.40/$2.00
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 is the newer of the two.
General reasoning, coding and multimodal tasks
Mistral Medium 3
GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
Efficient mid-tier deployment for production workloads
Mistral Medium 3
Mistral Medium 3 lists efficient mid-tier deployment for production workloads among its strengths; GPT-4o mini does not.
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.
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, and on millions of tokens that margin decides the monthly bill.
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 cost-to-capability at $0.40/$2.00
→ Mistral Medium 3
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-4o mini or Mistral Medium 3
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: 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: 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.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GPT-4o mini (US) and Mistral Medium 3 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. GPT-4o mini 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 GPT-4o mini and Mistral Medium 3 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 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 leans toward strong cost-to-capability at $0.40/$2.00, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or Mistral Medium 3?
GPT-4o mini is cheaper — $0.15/$0.6 per 1M tokens vs $0.4/$2 per 1M tokens, roughly 2.7× apart on input.
Which has the bigger context window?
Both advertise 128K (~192 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-4o mini and Mistral Medium 3 together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, Mistral Medium 3 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?
Mistral Medium 3 — released May 7, 2025, about 10 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.