GPT-5.6 Sol vs GPT-5.6 Terra
GPT-5.6 Sol wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose GPT-5.6 Sol if you…
- Enhanced reasoning
- Strong code generation
- o-series architecture
- Best for: Reasoning-heavy tasks, Coding, Math
Choose GPT-5.6 Terra if you…
- OpenAI quality
- Halved price
- Rich ecosystem
- Best for: Value, General tasks, OpenAI ecosystem
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | GPT-5.6 Sol | GPT-5.6 Terra | Verdict |
|---|---|---|---|
| Vendor | OpenAI (US) | OpenAI (US) | Same vendor |
| Released | 2026.07 | 2026.07 | GPT-5.6 Terra is newer |
| Overall (rank) | 72 · #11 | 62 · #21 | GPT-5.6 Sol +10 |
| Coding | 88 · #3 | 70 · #12 | GPT-5.6 Sol +18 |
| Multimodal | 80 · #11 | 69 · #19 | GPT-5.6 Sol +11 |
| Context window | 1.05M | 1.05M | Tie |
| Max output | 128K | 128K | Tie |
| Effective-context | 88 | 88 | Tie |
| Input $/1M | $5 | $2.5 | GPT-5.6 Terra cheaper |
| Output $/1M | $30 | $15 | GPT-5.6 Terra cheaper |
| Cache discount | 50% off | 50% off | Tie |
| Speed | ~25 tok/s | ~60 tok/s | GPT-5.6 Terra faster |
| TTFT | 2.0s | 0.5s | GPT-5.6 Terra snappier |
| Function calling | 93 | 90 | GPT-5.6 Sol ahead |
| Refusal rate | ~14% | ~13% | GPT-5.6 Terra less restrictive |
| English | 92 | 88 | GPT-5.6 Sol |
| Chinese | 74 | 72 | GPT-5.6 Sol |
| Modalities | text, image | text, image | Same |
| Open weights | No | No | Both closed |
| Fine-tuning | No | No | |
| Free tier | No free API tier | No free API | |
| SOC2 / no-train | yes / yes | yes / yes | |
| Private deployment | No | No | Neither |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
GPT-5.6 Sol leads the overall aggregate by 10 points (72 vs 62). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GPT-5.6 Terra remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a clear gap: GPT-5.6 Sol scores 88 against 70. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-5.6 Sol needs fewer correction turns; GPT-5.6 Terra is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GPT-5.6 Sol leads multimodal 80 vs 69. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
GPT-5.6 Terra is faster in interactive use: ~60 tok/s with 0.5s TTFT versus ~25 tok/s with 2.0s TTFT (about 2.4× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1.05M for GPT-5.6 Sol and 1.05M for GPT-5.6 Terra. Effective-context scores point the same way as window size — GPT-5.6 Sol is ahead on usable recall (88 vs 88), so prefer it for long-document work where details cannot be missed.
Price & total cost
GPT-5.6 Terra is the cheaper API at $2.5/$15 versus GPT-5.6 Sol at $5/$30 per 1M input/output tokens — list input is about 2.0× lower. Cache discounts (GPT-5.6 Sol 50% vs GPT-5.6 Terra 50%) shift the effective bill, worked out below.
Chinese vs English
English: GPT-5.6 Sol 92 vs GPT-5.6 Terra 88. Chinese: 74 vs 72. Both are US-based models; for Chinese-first workloads also compare domestic models on the leaderboard.
Tool use & ecosystem
Function-calling score: GPT-5.6 Sol 93 vs GPT-5.6 Terra 90, so GPT-5.6 Sol has the edge on structured tool use. Neither offers standard fine-tuning. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | GPT-5.6 Sol | GPT-5.6 Terra | Gap |
|---|---|---|---|
| List priceno cache applied | $1,400100M in × $5 + 30M out × $30 | $700100M in × $2.5 + 30M out × $15 | 2.00×gap |
| With caching90% of inputs cache-hit | $1,17590M cached in × $2.5 + 10M in × $5 + 30M out × $30 | $58890M cached in × $1.25 + 10M in × $2.5 + 30M out × $15 | 2.00×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.