Claude Fable 5 vs GPT-5.6 Terra
At a Glance
| Compare | Claude Fable 5Anthropic | GPT-5.6 TerraOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | UnrankedNot in the 57-model eligible cohort | #10 of 5777.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 27-model eligible cohort | #23 of 27$0.266 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | UnrankedNot in the 27-model eligible cohort | #16 of 2748.0 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $10.00Anthropic ↗ · Sep 3, 2026 | $2.00Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $50.00Anthropic ↗ · Sep 3, 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 | Claude Fable 5 | GPT-5.6 Terra |
|---|---|---|
| ARC-AGI-1verified-v1-613c385b0c93 · verified_score · leader | 98.50100% of row best · percent · Claude Fable 5 (XHigh) | 96.5098% of row best · percent · GPT-5.6 Terra (Max) |
| ARC-AGI-2verified-v2-be40992860f9 · verified_score · leader | 89.17100% of row best · percent · Claude Fable 5 (Max) | 83.9094% of row best · percent · GPT-5.6 Terra (Max) |
| LMArena Document Arenadocument-2026-09-13-0ebb411abf00 · arena_rating · leader | 1,496.08100% of row best · rating · claude-fable-5; 95% CI [1488.22135990, 1503.94311259]; votes 8497; rank 5 | 1,471.5898% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1463.31179298, 1479.85049651]; votes 5787; rank 13 |
| LMArena Vision Arenavision-2026-10-09-0ebb411abf00 · arena_rating · leader | 1,323.95100% of row best · rating · claude-fable-5-high; 95% CI [1316.82313713, 1331.08571856]; votes 14175; rank 1 | 1,271.0796% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1263.65540928, 1278.48964371]; votes 11020; rank 43 |
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 86.55100% of row best · percent · claude-fable-5-max-effort · 20,255 output tokens / case | 82.3195% of row best · percent · gpt-5.6-terra-max · 22,145 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 81.90100% of row best · points · Claude Fable 5 (high) · 24,076 output tokens / case | 81.4099% of row best · points · GPT-5.6 Terra (ultra) · 20,718 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 5 benchmark winsOverall 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.
Side-by-Side Facts
| Field | Claude Fable 5 | GPT-5.6 Terra |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 5 | Gpt 5 6 |
| Model | Claude Fable 5 | GPT-5.6 Terra |
| Version | Claude Fable 5 | GPT-5.6 Terra |
| Lifecycle | active | active |
| Released | 2026-06-09 | 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 | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Anthropic (Standard), Deepinfra (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Claude Fable 5 Capabilities
GPT-5.6 Terra Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Fable 5 vs GPT-5.6 Terra FAQs
Is Claude Fable 5 or GPT-5.6 Terra better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Fable 5 and GPT-5.6 Terra, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Fable 5 or GPT-5.6 Terra?+
Claude Fable 5 is $10.00 and GPT-5.6 Terra is $2.00 per million tokens, so GPT-5.6 Terra is cheaper on this metric. Claude Fable 5 is $50.00 and GPT-5.6 Terra is $12.00 per million tokens, so GPT-5.6 Terra is cheaper on this metric.
Which has a larger context window, Claude Fable 5 or GPT-5.6 Terra?+
GPT-5.6 Terra has the larger sourced context window. Claude Fable 5 supports 1,000,000 tokens and GPT-5.6 Terra supports 1,050,000 tokens.
Which performs better in benchmarks, Claude Fable 5 or GPT-5.6 Terra?+
Claude Fable 5 leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can Claude Fable 5 or GPT-5.6 Terra be self-hosted?+
Both models have the same sourced self-hosting status: unsupported. Claude Fable 5 is not marked open weight; GPT-5.6 Terra is not marked open weight.
Can Claude Fable 5 and GPT-5.6 Terra understand images?+
Claude Fable 5 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, Claude Fable 5 or GPT-5.6 Terra?+
Both models have the same sourced maximum output: 128,000 tokens each.
Do Claude Fable 5 and GPT-5.6 Terra support reasoning and tool use?+
Claude Fable 5: reasoning, 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, Claude Fable 5 or GPT-5.6 Terra?+
Claude Fable 5 has 3 sourced provider routes; GPT-5.6 Terra has 2, so Claude Fable 5 has broader tracked availability.
Which offers better value, Claude Fable 5 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.