GLM-5.3 vs GPT-6.1 Sol vs Claude Opus 5.5
GPT-6.1 Sol comes out ahead, 72 to 69 and 63 on our weighted score, though GLM-5.3 is 46% cheaper per token.
Z.ai (Zhipu)GLM-5.3
63/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
- Our pick
OpenAIGPT-6.1 Sol
72/100- ECI167.8
- Price$2.00 / $10.00
- Context1.05M
AnthropicClaude Opus 5.5
69/100- ECI168.4
- Price$4.00 / $20.00
- Context1M
GPT-6.1 Sol is our pick
GPT-6.1 Sol is the better all-round choice, scoring 72/100 against Claude Opus 5.5 (69) and GLM-5.3 (63). GLM-5.3 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityClaude Opus 5.5Capabilities Index (ECI): Claude Opus 5.5 168.4 · GPT-6.1 Sol 167.8 · GLM-5.3 156.2
- Lowest priceGLM-5.3GLM-5.3 $2.15 · GPT-6.1 Sol $4.00 · Claude Opus 5.5 $8.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6.1 SolGPT-6.1 Sol 1,050,000 · GLM-5.3 1,000,000 · Claude Opus 5.5 1,000,000 tokens
- Widest inputsGPT-6.1 Sol and Claude Opus 5.5GLM-5.3: Text · GPT-6.1 Sol: Text, Images, PDFs · Claude Opus 5.5: Text, Images, PDFs
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | GPT-6.1 Sol | Claude Opus 5.5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 99 | 99 |
| Price | 25% | 33 | 20 | 7 |
| Inputs & features | 15% | 45 | 80 | 80 |
| Context window | 10% | 60 | 61 | 60 |
| Overall | 100% | 63/100 | 72/100 | 69/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | GPT-6.1 SolOpenAI | Claude Opus 5.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 | 167.8 | 168.4 (best) |
| ECI rank | #33 of 165 | #4 of 165 | #2 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 90.9% | 95.4% (best) | 90.6% |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | 93.7% (best) | 91.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 100% (best) | 100% (best) |
| SimpleQA VerifiedShort factual questions | 41.0% | 73.9% (best) | 72.2% |
| Price per million tokens | |||
| Input | $1.40 (best) | $2.00 | $4.00 |
| Output | $4.40 (best) | $10.00 | $20.00 |
| Cached input | $0.26 | $0.10 (best) | $0.20 |
| Blended (3:1) | $2.15 (best) | $4.00 | $8.00 |
| Long-context rate | Same rate | Over 272K: $4.00 / $15.00 | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Official Anthropic API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,050,000 tokens (best) | 1,000,000 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · high · max | Yeslow · medium · high · xhigh · max | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.3 | gpt-6.1-sol | claude-opus-5-5 |
| API providers | 83 (best) | 33 | 36 |
| Released | Aug 14, 2026 | Sep 29, 2026 | Sep 22, 2026 |
| Knowledge cutoff | — | Apr 30, 2026 | 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.
- GLM-5.3$22.80
- GPT-6.1 Sol$40.00
- Claude Opus 5.5$80.00
Which should you choose?
Which is better: GLM-5.3, GPT-6.1 Sol or Claude Opus 5.5?
GPT-6.1 Sol is the better all-round choice, scoring 72/100 against Claude Opus 5.5 (69) and GLM-5.3 (63). GLM-5.3 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3, GPT-6.1 Sol or Claude Opus 5.5?
GLM-5.3 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). GPT-6.1 Sol costs $2.00 input / $10.00 output per million tokens (official OpenAI API price); Claude Opus 5.5 costs $4.00 input / $20.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $2.15 per million tokens for GLM-5.3 versus $4.00 for GPT-6.1 Sol (1.9× as much) and $8.00 for Claude Opus 5.5 (3.7× as much).
Which scores higher on benchmarks?
Claude Opus 5.5 scores higher on the Capabilities Index (ECI): Claude Opus 5.5 168.4 (#2 of 165), GPT-6.1 Sol 167.8 (#4 of 165) and GLM-5.3 156.2 (#33 of 165). The confidence ranges of the top two overlap (165.4–173.0 vs 164.3–172.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-6.1 Sol 95.4%, GLM-5.3 90.9%, Claude Opus 5.5 90.6%; FrontierMath Tiers 1–3 — GPT-6.1 Sol 93.7%, Claude Opus 5.5 91.2%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — GPT-6.1 Sol 100%, Claude Opus 5.5 100%, GLM-5.3 91.1%; SimpleQA Verified — GPT-6.1 Sol 73.9%, Claude Opus 5.5 72.2%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, GPT-6.1 Sol and Claude Opus 5.5 yet, so there is no like-for-like coding score. On overall capability, Claude Opus 5.5 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-6.1 Sol has the largest context window at 1,050,000 tokens, against 1,000,000 for GLM-5.3 and 1,000,000 for Claude Opus 5.5. Maximum output per response: GLM-5.3 up to 131,072, GPT-6.1 Sol up to 128,000, Claude Opus 5.5 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; GPT-6.1 Sol accepts text, images and PDFs; Claude Opus 5.5 accepts text, images and PDFs. GPT-6.1 Sol handles the widest range of inputs.
Are any of these open source?
GLM-5.3 publishes its weights and can be self-hosted; GPT-6.1 Sol and Claude Opus 5.5 is proprietary.
Which is newer?
GPT-6.1 Sol is the newest, released Sep 29, 2026. Claude Opus 5.5 came out Sep 22, 2026; GLM-5.3 came out Aug 14, 2026. Knowledge cutoff: GPT-6.1 Sol Apr 30, 2026, Claude Opus 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 Nov 2, 2026.
