Claude Opus 5 vs Trinity Large Thinking vs GPT-5.6 Sol
Trinity Large Thinking comes out ahead, 55 to 40 and 37 on our weighted score, and it is the cheaper option too.
AnthropicClaude Opus 5
37/100- ECI162.9
- Price$5.00 / $25.00
- Context1M
- Our pick
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
OpenAIGPT-5.6 Sol
40/100- ECI161.8
- Price$4.00 / $20.00
- Context1.05M
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 55/100 against GPT-5.6 Sol (40) and Claude Opus 5 (37). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceTrinity Large ThinkingTrinity Large Thinking $0.388 · GPT-5.6 Sol $8.00 · Claude Opus 5 $10.00 per 1M tokens (3:1 blend)
- Longest contextGPT-5.6 SolGPT-5.6 Sol 1,050,000 · Claude Opus 5 1,000,000 · Trinity Large Thinking 524,288 tokens
- Widest inputsClaude Opus 5 and GPT-5.6 SolClaude Opus 5: Text, Images, PDFs · Trinity Large Thinking: Text · GPT-5.6 Sol: Text, Images, PDFs
- Self-hostingTrinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Claude Opus 5 | Trinity Large Thinking | GPT-5.6 Sol |
|---|---|---|---|---|
| Price | 50% | 2 | 69 | 7 |
| Inputs & features | 30% | 80 | 35 | 80 |
| Context window | 20% | 60 | 49 | 61 |
| Overall | 100% | 37/100 | 55/100 | 40/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Opus 5Anthropic | Trinity Large ThinkingArcee AI | GPT-5.6 SolOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 162.9 (best) | — | 161.8 |
| ECI rank | #5 of 148 (best) | — | #8 of 148 |
| GPQA DiamondGraduate-level science questions | 93.9% (best) | — | 93.5% |
| FrontierMath Tiers 1–3Research-level mathematics | 85.6% | — | 89.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 98.9% | — | 100% (best) |
| SimpleQA VerifiedShort factual questions | 59.9% | — | 69.7% (best) |
| Price per million tokens | |||
| Input | $5.00 | $0.25 (best) | $4.00 |
| Output | $25.00 | $0.80 (best) | $20.00 |
| Cached input | $0.50 | $0.06 (best) | $0.40 |
| Blended (3:1) | $10.00 | $0.388 (best) | $8.00 |
| Long-context rate | Same rate | Same rate | Over 272K: $8.00 / $30.00 |
| Price source | Official Anthropic API | Official Arcee API | Official OpenAI API |
| Limits | |||
| Context window | 1,000,000 tokens | 524,288 tokens | 1,050,000 tokens (best) |
| Max output | 128,000 tokens | 262,144 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenOpenMDW-1.1 | Proprietary |
| API model ID | claude-opus-5 | trinity-large-thinking | gpt-5.6-sol |
| API providers | 35 | 6 | 40 (best) |
| Released | Jul 24, 2026 | Apr 1, 2026 | Jul 9, 2026 |
| Knowledge cutoff | May 2026 | — | Feb 16, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Claude Opus 5$100.00
- Trinity Large Thinking$4.10
- GPT-5.6 Sol$80.00
Which should you choose?
Which is better: Claude Opus 5, Trinity Large Thinking or GPT-5.6 Sol?
Trinity Large Thinking is the better all-round choice, scoring 55/100 against GPT-5.6 Sol (40) and Claude Opus 5 (37). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Claude Opus 5, Trinity Large Thinking or GPT-5.6 Sol?
Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI API price); Claude Opus 5 costs $5.00 input / $25.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $8.00 for GPT-5.6 Sol (21× as much) and $10.00 for Claude Opus 5 (26× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Claude Opus 5 has an ECI of 162.9, Trinity Large Thinking has not been scored yet and GPT-5.6 Sol has an ECI of 161.8.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Opus 5, Trinity Large Thinking and GPT-5.6 Sol yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5.6 Sol has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Opus 5 and 524,288 for Trinity Large Thinking. Maximum output per response: Claude Opus 5 up to 128,000, Trinity Large Thinking up to 262,144, GPT-5.6 Sol up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Claude Opus 5 accepts text, images and PDFs; Trinity Large Thinking accepts text; GPT-5.6 Sol accepts text, images and PDFs. Claude Opus 5 handles the widest range of inputs.
Are any of these open source?
Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; Claude Opus 5 and GPT-5.6 Sol is proprietary.
Which is newer?
Claude Opus 5 is the newest, released Jul 24, 2026. GPT-5.6 Sol came out Jul 9, 2026; Trinity Large Thinking came out Apr 1, 2026. Knowledge cutoff: Claude Opus 5 May 2026, GPT-5.6 Sol Feb 16, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
