Mistral Medium 3.5 128B vs GPT-4.1 Nano
At a Glance
| Compare |  Mistral Medium 3.5 128BMistral AI |  GPT-4.1 NanoOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.050Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.20Openrouter ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 262K | 1,048K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Mistral-Medium-3.5-128B | GPT-4.1 Nano |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,420.58100% of row best · rating · mistral-medium-3.5; 95% CI [1414.04225396, 1427.11615611]; votes 10996; rank 104 | 1,284.7290% of row best · rating · gpt-4.1-nano-2025-04-14; 95% CI [1277.08013307, 1292.36659971]; votes 6103; rank 250 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,222.05100% of row best · rating · mistral-medium-3.5; 95% CI [1212.30064904, 1231.79518832]; votes 5090; rank 71 | 1,063.1487% of row best · rating · gpt-4.1-nano-2025-04-14; 95% CI [1044.85853307, 1081.42105669]; votes 1211; rank 124 |
| Overall ResultCounted from the protocol-matched rows above | 2 benchmark winsNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Mistral-Medium-3.5-128B | GPT-4.1 Nano |
|---|---|---|
| Developer | Mistral AI | OpenAI |
| Family | Mistral Medium 3 5 128b | Gpt 4 1 |
| Model | Mistral-Medium-3.5-128B | GPT-4.1 Nano |
| Version | Mistral-Medium-3.5-128B | GPT-4.1 Nano |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | 2024-06-01 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,048K |
| Total parameters | 127.7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Mistral Medium 3.5 128B Capabilities
GPT-4.1 Nano Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Mistral-Medium-3.5-128BMistral AI | vs | GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
 Gemini 3.1 ProGoogle DeepMind | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs | Grok 4.6xAI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Mistral-Medium-3.5-128BMistral AI | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs | Mistral Large 3Mistral AI | family variantsimage, text |
Primary Evidence
Sources and Freshness
Questions
Mistral Medium 3.5 128B vs GPT-4.1 Nano FAQs
Is Mistral Medium 3.5 128B or GPT-4.1 Nano better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Medium 3.5 128B and GPT-4.1 Nano, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Mistral Medium 3.5 128B or GPT-4.1 Nano?+
Only GPT-4.1 Nano has a directly sourced input price: $0.050 per million tokens. Only GPT-4.1 Nano has a directly sourced output price: $0.20 per million tokens.
Which has a larger context window, Mistral Medium 3.5 128B or GPT-4.1 Nano?+
GPT-4.1 Nano has the larger sourced context window. Mistral Medium 3.5 128B supports 262K and GPT-4.1 Nano supports 1,048K.
Which performs better in benchmarks, Mistral Medium 3.5 128B or GPT-4.1 Nano?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Mistral Medium 3.5 128B or GPT-4.1 Nano be self-hosted?+
Mistral Medium 3.5 128B is the only model in this pair currently marked as self-hostable. Mistral Medium 3.5 128B is open weight; GPT-4.1 Nano is not marked open weight.
Can Mistral Medium 3.5 128B and GPT-4.1 Nano understand images?+
Mistral Medium 3.5 128B is documented with image input; GPT-4.1 Nano is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Mistral Medium 3.5 128B or GPT-4.1 Nano?+
Neither has a larger sourced maximum output. Mistral Medium 3.5 128B is — and GPT-4.1 Nano is 33K.
Do Mistral Medium 3.5 128B and GPT-4.1 Nano support reasoning and tool use?+
Mistral Medium 3.5 128B: reasoning, tool calling, and image input. GPT-4.1 Nano: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Mistral Medium 3.5 128B or GPT-4.1 Nano?+
Mistral Medium 3.5 128B has 0 sourced provider routes; GPT-4.1 Nano has 2, so GPT-4.1 Nano has broader tracked availability.
Which offers better value, Mistral Medium 3.5 128B or GPT-4.1 Nano?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.