Llama 4 Maverick 17B 128E Instruct vs Mistral Large 3
At a Glance
| Compare |  Mistral Large 3Mistral AI | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #43 of 570.0 score · 1/3 sources · single-source provisional · missing LiveBench, ARC-AGI-2 · full-core range 0.0–66.7 | #28 of 5743.0 score · 1/3 sources · single-source provisional · missing LiveBench, ARC-AGI-2 · full-core range 14.3–81.0 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.1875Openrouter ↗ · Oct 19, 2026 | $0.25Openrouter ↗ · Oct 19, 2026 |
| Output priceFrom · USD / 1M tokens | $0.6525Openrouter ↗ · Oct 19, 2026 | $0.75Openrouter ↗ · Oct 19, 2026 |
| Context windowMaximum documented tokens | 1,000K | 262K |
| 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 | Llama-4-Maverick-17B-128E-Instruct | Mistral Large 3 |
|---|---|---|
| LMArena Text Arenatext-2026-10-15-a64ca3b12b6e · arena_rating · leader | 1,287.1990% of row best · rating · llama-4-maverick-17b-128e-instruct; 95% CI [1282.93484207, 1291.44351277]; votes 39450; rank 261 | 1,428.16100% of row best · rating · mistral-large-3; 95% CI [1425.33671505, 1430.99053174]; votes 84205; rank 98 |
| LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e · arena_rating · leader | 1,141.6394% of row best · rating · llama-4-maverick-17b-128e-instruct; 95% CI [1132.58229133, 1150.67926500]; votes 6922; rank 120 | 1,220.76100% of row best · rating · mistral-large-3; 95% CI [1212.81813492, 1228.69335345]; votes 8643; rank 83 |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 69.93100% of row best · points · Llama 4 Maverick (no thinking) · 2,232 output tokens / case | 61.7888% of row best · points · Mistral Large 3 (no thinking) · 2,031 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 2 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 | Llama-4-Maverick-17B-128E-Instruct | Mistral Large 3 |
|---|---|---|
| Developer | Meta | Mistral AI |
| Family | Llama 4 Maverick 17b 128e Instruct | Mistral Large 3 |
| Model | Llama-4-Maverick-17B-128E-Instruct | Mistral Large 3 |
| Version | Llama-4-Maverick-17B-128E-Instruct | Mistral Large 3 |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2025-12-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Document |
| Output modalities | Text | Text |
| Context window | 1,000K | 262K |
| Total parameters | 401.6B | 675B |
| Active parameters | 17B | 41B |
| License | other | Apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard) | Mistral AI (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | agents, chat, generation, structured_outputs, tools, vision |
Llama 4 Maverick 17B 128E Instruct Capabilities
Mistral Large 3 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Maverick 17B 128E Instruct vs Mistral Large 3 FAQs
Is Llama 4 Maverick 17B 128E Instruct or Mistral Large 3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Maverick 17B 128E Instruct and Mistral Large 3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Maverick 17B 128E Instruct or Mistral Large 3?+
Llama 4 Maverick 17B 128E Instruct is $0.1875 and Mistral Large 3 is $0.25 per million tokens, so Llama 4 Maverick 17B 128E Instruct is cheaper on this metric. Llama 4 Maverick 17B 128E Instruct is $0.6525 and Mistral Large 3 is $0.75 per million tokens, so Llama 4 Maverick 17B 128E Instruct is cheaper on this metric.
Which has a larger context window, Llama 4 Maverick 17B 128E Instruct or Mistral Large 3?+
Llama 4 Maverick 17B 128E Instruct has the larger sourced context window. Llama 4 Maverick 17B 128E Instruct supports 1,000,000 tokens and Mistral Large 3 supports 262,144 tokens.
Which performs better in benchmarks, Llama 4 Maverick 17B 128E Instruct or Mistral Large 3?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Llama 4 Maverick 17B 128E Instruct or Mistral Large 3 be self-hosted?+
Both models have the same sourced self-hosting status: supported. Llama 4 Maverick 17B 128E Instruct is open weight; Mistral Large 3 is open weight.
Can Llama 4 Maverick 17B 128E Instruct and Mistral Large 3 understand images?+
Llama 4 Maverick 17B 128E Instruct is documented with image input; Mistral Large 3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Maverick 17B 128E Instruct or Mistral Large 3?+
Neither model has a sourced maximum output in this comparison, so a larger limit cannot be determined.
Do Llama 4 Maverick 17B 128E Instruct and Mistral Large 3 support reasoning and tool use?+
Llama 4 Maverick 17B 128E Instruct: tool calling and image input. Mistral Large 3: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Maverick 17B 128E Instruct or Mistral Large 3?+
Llama 4 Maverick 17B 128E Instruct has 1 sourced provider route; Mistral Large 3 has 2, so Mistral Large 3 has broader tracked availability.
Which offers better value, Llama 4 Maverick 17B 128E Instruct or Mistral Large 3?+
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.