o4-mini vs Gemini 2.5 Pro vs GLM-5
Gemini 2.5 Pro comes out ahead, 63 to 59 and 55 on our weighted score, though GLM-5 is 2.2× cheaper per token.
OpenAIo4-mini
59/100- ECI145.6
- Price$1.10 / $4.40
- Context200K
- Our pick
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
Gemini 2.5 Pro is our pick
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against o4-mini (59) and GLM-5 (55). It leads on inputs & features and context window. GLM-5 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5Capabilities Index (ECI): GLM-5 145.8 · o4-mini 145.6 · Gemini 2.5 Pro 145.3
- Lowest priceGLM-5GLM-5 $1.55 · o4-mini $1.93 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · GLM-5 204,800 · o4-mini 200,000 tokens
- Widest inputsGemini 2.5 Proo4-mini: Text, Images · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · GLM-5: Text
- Self-hostingGLM-5Publishes downloadable weights
| Measure | Weight | o4-mini | Gemini 2.5 Pro | GLM-5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 72 | 73 |
| Price | 25% | 36 | 24 | 41 |
| Inputs & features | 15% | 70 | 100 | 35 |
| Context window | 10% | 32 | 61 | 32 |
| Overall | 100% | 59/100 | 63/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o4-miniOpenAI | Gemini 2.5 ProGoogle | GLM-5Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.6 | 145.3 | 145.8 (best) |
| ECI rank | #76 of 148 | #78 of 148 | #74 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 79.6% | 85.3% | 87.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 36.1% (best) | 24.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.7% | 84.7% (best) | 80.0% |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.6% | 72.1% (best) |
| SimpleQA VerifiedShort factual questions | 19.6% | — | — |
| Price per million tokens | |||
| Input | $1.10 | $1.25 | $1.00 (best) |
| Output | $4.40 | $10.00 | $3.20 (best) |
| Cached input | $0.275 | $0.125 (best) | $0.20 |
| Blended (3:1) | $1.93 | $3.44 | $1.55 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Official OpenAI API | Official Google API | Official Z.AI API |
| Limits | |||
| Context window | 200,000 tokens | 1,048,576 tokens (best) | 204,800 tokens |
| Max output | 100,000 tokens | 65,536 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | o4-mini | gemini-2.5-pro | glm-5 |
| API providers | 19 | 22 | 27 (best) |
| Released | Apr 16, 2025 | Jun 17, 2025 | Feb 12, 2026 |
| Knowledge cutoff | May 2024 | Jan 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o4-mini$19.80
- Gemini 2.5 Pro$32.50
- GLM-5$16.40
Which should you choose?
Which is better: o4-mini, Gemini 2.5 Pro or GLM-5?
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against o4-mini (59) and GLM-5 (55). It leads on inputs & features and context window. GLM-5 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, o4-mini, Gemini 2.5 Pro or GLM-5?
GLM-5 is cheaper at $1.00 input / $3.20 output per million tokens (official Z.AI API price). o4-mini costs $1.10 input / $4.40 output per million tokens (official OpenAI API price); Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $1.55 per million tokens for GLM-5 versus $1.93 for o4-mini (1.2× as much) and $3.44 for Gemini 2.5 Pro (2.2× as much).
Which scores higher on benchmarks?
GLM-5 scores higher on the Capabilities Index (ECI): GLM-5 145.8 (#74 of 148), o4-mini 145.6 (#76 of 148) and Gemini 2.5 Pro 145.3 (#78 of 148). The confidence ranges of the top two overlap (143.9–147.7 vs 143.0–147.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5 87.8%, Gemini 2.5 Pro 85.3%, o4-mini 79.6%; OTIS Mock AIME 2024–2025 — Gemini 2.5 Pro 84.7%, o4-mini 81.7%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for o4-mini yet, so there is no like-for-like coding score. On overall capability, GLM-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?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 204,800 for GLM-5 and 200,000 for o4-mini. Maximum output per response: o4-mini up to 100,000, Gemini 2.5 Pro up to 65,536, GLM-5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
o4-mini accepts text and images; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; GLM-5 accepts text. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
GLM-5 publishes its weights and can be self-hosted; o4-mini and Gemini 2.5 Pro is proprietary.
Which is newer?
GLM-5 is the newest, released Feb 12, 2026. Gemini 2.5 Pro came out Jun 17, 2025; o4-mini came out Apr 16, 2025. Knowledge cutoff: o4-mini May 2024, Gemini 2.5 Pro Jan 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.
