Llama 4 Maverick 17B 128E Instruct vs GPT-5.6 Terra
At a Glance
| Compare |  GPT-5.6 TerraOpenAI | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #13 of 4674.9 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #35 of 44$0.266 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #23 of 3851.5 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.1875Openrouter ↗ · Sep 22, 2026 | $2.00Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.6525Openrouter ↗ · Sep 22, 2026 | $12.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,000K | 1,050K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 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 | GPT-5.6 Terra |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,287.1789% of row best · rating · llama-4-maverick-17b-128e-instruct; 95% CI [1282.91290700, 1291.42929740]; votes 39359; rank 247 | 1,446.22100% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1441.41414497, 1451.02563541]; votes 28119; rank 52 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,141.5790% of row best · rating · llama-4-maverick-17b-128e-instruct; 95% CI [1132.52682180, 1150.61627536]; votes 6937; rank 106 | 1,269.90100% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1261.54272100, 1278.25950723]; votes 7726; rank 36 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 62.4572% of row best · points · Llama 4 Maverick · 1,154 output tokens / case | 86.39100% of row best · points · GPT-5.6 Terra (ultra) · 11,336 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 | GPT-5.6 Terra |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Llama 4 Maverick 17b 128e Instruct | Gpt 5 6 |
| Model | Llama-4-Maverick-17B-128E-Instruct | GPT-5.6 Terra |
| Version | Llama-4-Maverick-17B-128E-Instruct | GPT-5.6 Terra |
| Lifecycle | active | active |
| Released | 2025-04-05 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 1,050K |
| Total parameters | 401.6B | Unknown |
| Active parameters | 17B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
Llama 4 Maverick 17B 128E Instruct Capabilities
GPT-5.6 Terra Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Maverick 17B 128E Instruct vs GPT-5.6 Terra FAQs
Is Llama 4 Maverick 17B 128E Instruct or GPT-5.6 Terra better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Maverick 17B 128E Instruct and GPT-5.6 Terra, 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 GPT-5.6 Terra?+
Llama 4 Maverick 17B 128E Instruct is $0.1875 and GPT-5.6 Terra is $2.00 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 GPT-5.6 Terra is $12.00 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 GPT-5.6 Terra?+
GPT-5.6 Terra has the larger sourced context window. Llama 4 Maverick 17B 128E Instruct supports 1,000K and GPT-5.6 Terra supports 1,050K.
Which performs better in benchmarks, Llama 4 Maverick 17B 128E Instruct or GPT-5.6 Terra?+
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 GPT-5.6 Terra be self-hosted?+
Llama 4 Maverick 17B 128E Instruct is the only model in this pair currently marked as self-hostable. Llama 4 Maverick 17B 128E Instruct is open weight; GPT-5.6 Terra is not marked open weight.
Can Llama 4 Maverick 17B 128E Instruct and GPT-5.6 Terra understand images?+
Llama 4 Maverick 17B 128E Instruct is documented with image input; GPT-5.6 Terra 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 GPT-5.6 Terra?+
Neither has a larger sourced maximum output. Llama 4 Maverick 17B 128E Instruct is — and GPT-5.6 Terra is 128K.
Do Llama 4 Maverick 17B 128E Instruct and GPT-5.6 Terra support reasoning and tool use?+
Llama 4 Maverick 17B 128E Instruct: tool calling and image input. GPT-5.6 Terra: reasoning, 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 GPT-5.6 Terra?+
Llama 4 Maverick 17B 128E Instruct has 1 sourced provider route; GPT-5.6 Terra has 2, so GPT-5.6 Terra has broader tracked availability.
Which offers better value, Llama 4 Maverick 17B 128E Instruct or GPT-5.6 Terra?+
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.