GLM-5.1 vs o3 vs Claude Sonnet 4.5
Too close to call on our weighted score (o3 58, GLM-5.1 57, Claude Sonnet 4.5 55). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
OpenAIo3
58/100- ECI146.9
- Price$2.00 / $8.00
- Context200K
AnthropicClaude Sonnet 4.5
55/100- ECI146.8
- Price$3.00 / $15.00
- Context200K
Too close to call
It is close. Our weighted score puts them within a point (o3 58/100, GLM-5.1 57/100, Claude Sonnet 4.5 55/100), so choose by what matters most for your work: GLM-5.1 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.1Capabilities Index (ECI): GLM-5.1 149.9 · o3 146.9 · Claude Sonnet 4.5 146.8
- Lowest priceGLM-5.1GLM-5.1 $2.15 · o3 $3.50 · Claude Sonnet 4.5 $6.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGLM-5.1 200,000 · o3 200,000 · Claude Sonnet 4.5 200,000 tokens
- Widest inputso3 and Claude Sonnet 4.5GLM-5.1: Text · o3: Text, Images, PDFs · Claude Sonnet 4.5: Text, Images, PDFs
- Self-hostingGLM-5.1Publishes downloadable weights
| Measure | Weight | GLM-5.1 | o3 | Claude Sonnet 4.5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 74 | 74 |
| Price | 25% | 34 | 24 | 13 |
| Inputs & features | 15% | 45 | 80 | 80 |
| Context window | 10% | 32 | 32 | 32 |
| Overall | 100% | 57/100 | 58/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.1Z.ai (Zhipu) | o3OpenAI | Claude Sonnet 4.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 (best) | 146.9 | 146.8 |
| ECI rank | #51 of 148 (best) | #63 of 148 | #64 of 148 |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 81.8% | 82.3% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% (best) | 33.3% | 23.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% (best) | 84.4% | 77.8% |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% (best) | 62.3% | 71.3% |
| SimpleQA VerifiedShort factual questions | 34.0% | 49.4% (best) | 30.7% |
| Price per million tokens | |||
| Input | $1.40 (best) | $2.00 | $3.00 |
| Output | $4.40 (best) | $8.00 | $15.00 |
| Cached input | $0.26 (best) | $0.50 | $0.30 |
| Blended (3:1) | $2.15 (best) | $3.50 | $6.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Official Anthropic API |
| Limits | |||
| Context window | 200,000 tokens | 200,000 tokens | 200,000 tokens |
| Max output | 131,072 tokens (best) | 100,000 tokens | 64,000 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 | Yes | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.1 | o3 | claude-sonnet-4-5 |
| API providers | 40 (best) | 18 | 28 |
| Released | Apr 7, 2026 | Apr 16, 2025 | Sep 29, 2025 |
| Knowledge cutoff | — | May 2024 | Jul 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.1$22.80
- o3$36.00
- Claude Sonnet 4.5$60.00
Which should you choose?
Which is better: GLM-5.1, o3 or Claude Sonnet 4.5?
It is close. Our weighted score puts them within a point (o3 58/100, GLM-5.1 57/100, Claude Sonnet 4.5 55/100), so choose by what matters most for your work: GLM-5.1 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, o3 or Claude Sonnet 4.5?
GLM-5.1 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). o3 costs $2.00 input / $8.00 output per million tokens (official OpenAI API price); Claude Sonnet 4.5 costs $3.00 input / $15.00 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-5.1 versus $3.50 for o3 (1.6× as much) and $6.00 for Claude Sonnet 4.5 (2.8× as much).
Which scores higher on benchmarks?
GLM-5.1 scores higher on the Capabilities Index (ECI): GLM-5.1 149.9 (#51 of 148), o3 146.9 (#63 of 148) and Claude Sonnet 4.5 146.8 (#64 of 148). The confidence ranges of the top two overlap (148.0–151.6 vs 144.9–148.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Claude Sonnet 4.5 82.3%, o3 81.8%; FrontierMath Tiers 1–3 — GLM-5.1 36.8%, o3 33.3%, Claude Sonnet 4.5 23.9%; OTIS Mock AIME 2024–2025 — GLM-5.1 93.3%, o3 84.4%, Claude Sonnet 4.5 77.8%; SWE-bench Verified — GLM-5.1 74.2%, Claude Sonnet 4.5 71.3%, o3 62.3%; SimpleQA Verified — o3 49.4%, GLM-5.1 34.0%, Claude Sonnet 4.5 30.7%.
Which is better for coding?
GLM-5.1 resolves more real GitHub issues on SWE-bench Verified: GLM-5.1 74.2%, Claude Sonnet 4.5 71.3% and o3 62.3%. All three support tool calling for agent workflows.
Which has the bigger context window?
GLM-5.1, o3 and Claude Sonnet 4.5 share the same 200,000-token context window. Maximum output per response: GLM-5.1 up to 131,072, o3 up to 100,000, Claude Sonnet 4.5 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; o3 accepts text, images and PDFs; Claude Sonnet 4.5 accepts text, images and PDFs. o3 handles the widest range of inputs.
Are any of these open source?
GLM-5.1 publishes its weights and can be self-hosted; o3 and Claude Sonnet 4.5 is proprietary.
Which is newer?
GLM-5.1 is the newest, released Apr 7, 2026. Claude Sonnet 4.5 came out Sep 29, 2025; o3 came out Apr 16, 2025. Knowledge cutoff: o3 May 2024, Claude Sonnet 4.5 Jul 31, 2025.
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.
