Mistral Large 3 vs GPT-5.4
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Mistral Large 3 | GPT-5.4 |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.2 | UnrankedNot in the 22-model eligible cohort | #9 of 2272.8 score |
| CostLower is better · Published-token output estimate | UnrankedNot in the 36-model eligible cohort | #30 of 36$0.274 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.0 | UnrankedNot in the 22-model eligible cohort | #15 of 2249.1 score |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | Mistral Large 3 | GPT-5.4 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,427.6298% of row best · rating · mistral-large-3; 95% CI [1424.57601414, 1430.66670291]; votes 65336; rank 85 | 1,452.51100% of row best · rating · gpt-5.4; 95% CI [1448.73439083, 1456.28324387]; votes 63615; rank 37 |
| LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · leader | 1,226.8495% of row best · rating · mistral-large-3; 95% CI [1216.90651393, 1236.78308197]; votes 4751; rank 66 | 1,293.18100% of row best · rating · gpt-5.4; 95% CI [1286.41064849, 1299.95761489]; votes 21245; rank 20 |
| ToneBench2026-08-28-10-task-cd9819ab6e4d · overall_score · leader | 70.3985% of row best · points · Mistral Large 3 · 2,056 output tokens / case | 82.47100% of row best · points · GPT-5.4 · 3,254 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 3 benchmark winsOverall lead |
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-5.4 |
|---|---|---|
| Developer | Mistral AI | OpenAI |
| Family | Mistral Large 3 | Gpt 5 4 |
| Model | Mistral Large 3 | GPT-5.4 |
| Version | Mistral Large 3 | GPT-5.4 |
| Lifecycle | active | active |
| Released | 2025-12-02 | 2026-03-05 |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text, Image, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,050K |
| 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, reasoning, structured_outputs, tools |
14 comparable fields · 11 material differences · Pair passes the primary-source comparison gate
Mistral Large 3 Capabilities
GPT-5.4 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
Mistral Large 3Mistral AI | vs | GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Mistral Large 3Mistral AI | cross-developer peersimage, text |
 Gemini 3.1 ProGoogle DeepMind | vs | Mistral Large 3Mistral AI | cross-developer peerstext |
 DeepSeek-V4-ProDeepSeek | vs | Mistral Large 3Mistral AI | cross-developer peerstext |
Mistral Large 3Mistral AI | vs | Grok 4.6xAI | cross-developer peersimage, text |
Mistral Large 3Mistral AI | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Mistral Large 3Mistral AI | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Mistral Large 3Mistral AI | cross-developer peersimage, text |
Mistral Large 3Mistral AI | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Mistral Large 3Mistral AI | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Mistral Large 3Mistral AI | cross-developer peersimage, text |
| vs | Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Mistral Large 3 vs GPT-5.4 FAQs
Is Mistral Large 3 or GPT-5.4 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Large 3 and GPT-5.4, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Mistral Large 3 or GPT-5.4?+
Mistral Large 3 is $0.50 and GPT-5.4 is $2.50 per million tokens, so Mistral Large 3 is cheaper on this metric. Mistral Large 3 is $1.50 and GPT-5.4 is $15.00 per million tokens, so Mistral Large 3 is cheaper on this metric.
Which has a larger context window, Mistral Large 3 or GPT-5.4?+
GPT-5.4 has the larger sourced context window. Mistral Large 3 supports 262K and GPT-5.4 supports 1,050K.
Which performs better in benchmarks, Mistral Large 3 or GPT-5.4?+
GPT-5.4 leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Mistral Large 3 or GPT-5.4 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-5.4 is not marked open weight.
Can Mistral Large 3 and GPT-5.4 understand images?+
Mistral Large 3 is documented with image input; GPT-5.4 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Mistral Large 3 or GPT-5.4?+
Neither has a larger sourced maximum output. Mistral Large 3 is — and GPT-5.4 is 128K.
Do Mistral Large 3 and GPT-5.4 support reasoning and tool use?+
Mistral Large 3: tool calling and image input. GPT-5.4: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Mistral Large 3 or GPT-5.4?+
Mistral Large 3 has 2 sourced provider routes; GPT-5.4 has 2, a tie.
Which offers better value, Mistral Large 3 or GPT-5.4?+
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.