Mistral Large 3 vs GPT-4.1
Benchmark Performance
Available Benchmarks
| Benchmark | Mistral Large 3 | GPT-4.1 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,427.62100% of row best · rating · mistral-large-3; 95% CI [1424.57601414, 1430.66670291]; votes 65336; rank 85 | 1,382.7197% of row best · rating · gpt-4.1-2025-04-14; 95% CI [1379.00467679, 1386.41941365]; votes 49913; rank 153 |
| LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · statistical tie | 1,226.84100% of row best · rating · mistral-large-3; 95% CI [1216.90651393, 1236.78308197]; votes 4751; rank 66 | 1,210.3299% of row best · rating · gpt-4.1-2025-04-14; 95% CI [1203.63813827, 1217.00696914]; votes 41034; rank 70 |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 1 benchmark winOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Mistral Large 3 | GPT-4.1 |
|---|---|---|
| Developer | Mistral AI | OpenAI |
| Family | Mistral Large 3 | Gpt 4 1 |
| Model | Mistral Large 3 | GPT-4.1 |
| Version | Mistral Large 3 | GPT-4.1 |
| Lifecycle | active | active |
| Released | 2025-12-02 | Unknown |
| Knowledge cutoff | Unknown | 2024-06-01 |
| Input modalities | Text, Image, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,048K |
| Total parameters | 675B | Unknown |
| Active parameters | 41B | Unknown |
| License | Apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Mistral AI (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | agents, chat, generation, structured_outputs, tools, vision | chat, generation, tools |
13 comparable fields · 10 material differences · Pair passes the primary-source comparison gate
Mistral Large 3 Capabilities
GPT-4.1 Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Mistral Large 3 vs GPT-4.1 FAQs
Is Mistral Large 3 or GPT-4.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Large 3 and GPT-4.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Mistral Large 3 or GPT-4.1?+
Mistral Large 3 is $0.50 and GPT-4.1 is $1.00 per million tokens, so Mistral Large 3 is cheaper on this metric. Mistral Large 3 is $1.50 and GPT-4.1 is $4.00 per million tokens, so Mistral Large 3 is cheaper on this metric.
Which has a larger context window, Mistral Large 3 or GPT-4.1?+
GPT-4.1 has the larger sourced context window. Mistral Large 3 supports 262K and GPT-4.1 supports 1,048K.
Which performs better in benchmarks, Mistral Large 3 or GPT-4.1?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Mistral Large 3 or GPT-4.1 be self-hosted?+
Mistral Large 3 is the only model in this pair currently marked as self-hostable. Mistral Large 3 is open weight; GPT-4.1 is not marked open weight.
Can Mistral Large 3 and GPT-4.1 understand images?+
Mistral Large 3 is documented with image input; GPT-4.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Mistral Large 3 or GPT-4.1?+
Neither has a larger sourced maximum output. Mistral Large 3 is — and GPT-4.1 is 33K.
Do Mistral Large 3 and GPT-4.1 support reasoning and tool use?+
Mistral Large 3: tool calling and image input. GPT-4.1: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Mistral Large 3 or GPT-4.1?+
Mistral Large 3 has 2 sourced provider routes; GPT-4.1 has 2, a tie.
Which offers better value, Mistral Large 3 or GPT-4.1?+
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.