GPT-6 Sol vs GPT-5.3 Codex vs Claude Sonnet 5.5
Too close to call on our weighted score (GPT-6 Sol 47, Claude Sonnet 5.5 47, GPT-5.3 Codex 42). The right pick depends on what you value most.
OpenAI
GPT-6 Sol
47/100- ECI—
- Price$2.00 / $10.00
- Context1.05M
OpenAI
GPT-5.3 Codex
42/100- ECI156.8
- Price$1.75 / $14.00
- Context400K
Anthropic
Claude Sonnet 5.5
47/100- ECI165.2
- Price$2.00 / $10.00
- Context1M
Too close to call
It is close. Our weighted score puts them within a point (GPT-6 Sol 47/100, Claude Sonnet 5.5 47/100, GPT-5.3 Codex 42/100), so choose by what matters most for your work: GPT-6 Sol on price and GPT-6 Sol for long inputs. 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 priceGPT-6 Sol and Claude Sonnet 5.5GPT-6 Sol $4.00 · Claude Sonnet 5.5 $4.00 · GPT-5.3 Codex $4.81 per 1M tokens (3:1 blend)
- Longest contextGPT-6 SolGPT-6 Sol 1,050,000 · Claude Sonnet 5.5 1,000,000 · GPT-5.3 Codex 400,000 tokens
- Widest inputsSame inputsGPT-6 Sol: Text, Images, PDFs · GPT-5.3 Codex: Text, Images, PDFs · Claude Sonnet 5.5: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-6 Sol | GPT-5.3 Codex | Claude Sonnet 5.5 |
|---|---|---|---|---|
| Price | 50% | 21 | 18 | 21 |
| Inputs & features | 30% | 80 | 80 | 80 |
| Context window | 20% | 61 | 44 | 60 |
| Overall | 100% | 47/100 | 42/100 | 47/100 |
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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 156.8 | 165.2 (best) |
| ECI rank | — | #18 of 148 | #3 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 94.3% | — | 95.6% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 89.8% (best) | — | 88.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | 100% |
| SWE-bench VerifiedFixing real GitHub issues | — | 74.8% | — |
| SimpleQA VerifiedShort factual questions | 60.7% (best) | — | 46.5% |
| Price per million tokens | |||
| Input | $2.00 | $1.75 (best) | $2.00 |
| Output | $10.00 (best) | $14.00 | $10.00 (best) |
| Cached input | $0.20 | $0.175 (best) | $0.20 |
| Blended (3:1) | $4.00 (best) | $4.81 | $4.00 (best) |
| Long-context rate | Over 272K: $4.00 / $15.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official OpenAI API | Official Anthropic API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 400,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yeslow · medium · high · xhigh | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-6-sol | gpt-5.3-codex | claude-sonnet-5-5 |
| API providers | 25 (best) | 19 | 23 |
| Released | Sep 22, 2026 | Feb 5, 2026 | Sep 28, 2026 |
| Knowledge cutoff | Apr 20, 2026 | Aug 31, 2025 | Jun 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
GPT-6 Sol$40.00
GPT-5.3 Codex$45.50
Claude Sonnet 5.5$40.00
Which should you choose?
Which is better: GPT-6 Sol, GPT-5.3 Codex or Claude Sonnet 5.5?
It is close. Our weighted score puts them within a point (GPT-6 Sol 47/100, Claude Sonnet 5.5 47/100, GPT-5.3 Codex 42/100), so choose by what matters most for your work: GPT-6 Sol on price and GPT-6 Sol for long inputs. 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, GPT-6 Sol, GPT-5.3 Codex or Claude Sonnet 5.5?
GPT-6 Sol is cheaper at $2.00 input / $10.00 output per million tokens (official OpenAI API price). Claude Sonnet 5.5 costs $2.00 input / $10.00 output per million tokens (official Anthropic API price); GPT-5.3 Codex costs $1.75 input / $14.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $4.00 per million tokens for GPT-6 Sol versus $4.00 for Claude Sonnet 5.5 (1× as much) and $4.81 for GPT-5.3 Codex (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-6 Sol has not been scored yet, GPT-5.3 Codex has an ECI of 156.8 and Claude Sonnet 5.5 has an ECI of 165.2.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Sol and Claude Sonnet 5.5 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-6 Sol has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Sonnet 5.5 and 400,000 for GPT-5.3 Codex. Maximum output per response: GPT-6 Sol up to 128,000, GPT-5.3 Codex up to 128,000, Claude Sonnet 5.5 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Sol accepts text, images and PDFs; GPT-5.3 Codex accepts text, images and PDFs; Claude Sonnet 5.5 accepts text, images and PDFs. They handle the same number of input types.
Are any of these open source?
No. GPT-6 Sol, GPT-5.3 Codex and Claude Sonnet 5.5 are proprietary and only available through APIs and apps.
Which is newer?
Claude Sonnet 5.5 is the newest, released Sep 28, 2026. GPT-6 Sol came out Sep 22, 2026; GPT-5.3 Codex came out Feb 5, 2026. Knowledge cutoff: GPT-6 Sol Apr 20, 2026, GPT-5.3 Codex Aug 31, 2025, Claude Sonnet 5.5 Jun 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.