Model comparison
GPT-5.6 Sol vs GPT-5.6 Terra
Two OpenAI tiers compared on the figures that decide which one a workload actually needs.
Catalog record checked August 12, 2026; individual provider fields may change.
OpenAI
GPT-5.6 Sol
Frontier
OpenAI
GPT-5.6 Terra
Balanced
| Specification | GPT-5.6 Sol | GPT-5.6 Terra |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Tier | Frontier | Balanced |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 128K |
| Input / 1M tokens | $5 | Winner: $2 |
| Output / 1M tokens | $30 | Winner: $12 |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | none, low, medium, high, xhigh, max | none, low, medium, high, xhigh, max |
| Modalities | text, image | text, image |
| Released | July 9, 2026 | July 9, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | Winner: 61 | 57 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: GPT-5.6 Sol: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. · GPT-5.6 Terra: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
GPT-5.6 Sol
OpenAI's flagship tier, aimed at the hardest reasoning and agentic work.
Best for
- Complex reasoning
- Agentic workflows
- Hard coding tasks
Watch out
25x Luna's input price — overspecified for routine generation. Sol Fast mode (where offered) is roughly ~2× price for ~2.5× speed — only enable when latency is the constraint. `gpt-5.2-chat-latest` / `gpt-5.3-chat-latest` shut down 2026-08-10 — migrate API chat traffic to Sol. ChatGPT Plus/Pro Chat got an Aug 6 Sol refresh + effort slider; Work/Codex/API July builds are unchanged.
GPT-5.6 Terra
OpenAI's middle tier, balancing capability against cost.
Best for
- Mixed workloads
- Teams standardising on one model
Watch out
10x Luna's input price; check whether Luna already suffices for the workload.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
GPT-5.6 Sol vs GPT-5.6 Terra
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.6 Sol or GPT-5.6 Terra cheaper for input?
GPT-5.6 Terra is cheaper at $2 per million input tokens, against $5 for GPT-5.6 Sol — roughly 2.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. GPT-5.6 Terra has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
Is GPT-5.6 Sol or GPT-5.6 Terra cheaper for output?
GPT-5.6 Terra is cheaper at $12 per million output tokens, against $30 for GPT-5.6 Sol — roughly 2.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. GPT-5.6 Terra has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
Which has the larger context window, GPT-5.6 Sol or GPT-5.6 Terra?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Do GPT-5.6 Sol and GPT-5.6 Terra support the same reasoning levels?
Yes — both accept the same effort settings: "none", "low", "medium", "high", "xhigh", "max". Higher effort costs more and takes longer, so start low and raise it only where output quality actually improves.
Should I use GPT-5.6 Sol or GPT-5.6 Terra?
GPT-5.6 Sol is the frontier tier and GPT-5.6 Terra the balanced tier. The useful question is whether your hardest task actually fails on the cheaper one — most production volume such as classification, extraction and summarisation does not.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.