MiniMax-M3 vs GLM-5
MiniMax-M3 comes out ahead, 69 to 55 on our weighted score, and it is the cheaper option too.
- Our pick
MiniMaxMiniMax-M3
69/100- ECI147.0
- Price$0.30 / $1.20
- Context1.05M
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
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Make it a three-way comparison.
MiniMax-M3 is our pick
MiniMax-M3 is the better all-round choice, scoring 69/100 against GLM-5 (55). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMiniMax-M3Capabilities Index (ECI): MiniMax-M3 147.0 · GLM-5 145.8
- Lowest priceMiniMax-M3MiniMax-M3 $0.525 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextMiniMax-M3MiniMax-M3 1,048,576 · GLM-5 204,800 tokens
- Widest inputsMiniMax-M3MiniMax-M3: Text, Images, Video · GLM-5: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | MiniMax-M3 | GLM-5 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 73 |
| Price | 25% | 63 | 41 |
| Inputs & features | 15% | 70 | 35 |
| Context window | 10% | 61 | 32 |
| Overall | 100% | 69/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M3MiniMax | GLM-5Z.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 147.0 (best) | 145.8 |
| ECI rank | #62 of 148 (best) | #74 of 148 |
| GPQA DiamondGraduate-level science questions | 90.9% (best) | 87.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 71.1% | 80.0% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 72.1% |
| Price per million tokens | ||
| Input | $0.30 (best) | $1.00 |
| Output | $1.20 (best) | $3.20 |
| Cached input | $0.06 (best) | $0.20 |
| Blended (3:1) | $0.525 (best) | $1.55 |
| Long-context rate | Over 512K: $0.60 / $2.40 | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Z.AI API |
| Limits | ||
| Context window | 1,048,576 tokens (best) | 204,800 tokens |
| Max output | 512,000 tokens (best) | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | Yes | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | MiniMax-M3 | glm-5 |
| API providers | 42 (best) | 27 |
| Released | Jun 1, 2026 | Feb 12, 2026 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M3$5.40
- GLM-5$16.40
Which should you choose?
Which is better: MiniMax-M3 or GLM-5?
MiniMax-M3 is the better all-round choice, scoring 69/100 against GLM-5 (55). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M3 or GLM-5?
MiniMax-M3 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M3 versus $1.55 for GLM-5 (3× as much).
Which scores higher on benchmarks?
MiniMax-M3 scores higher on the Capabilities Index (ECI): MiniMax-M3 147.0 (#62 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (142.7–149.8 vs 143.9–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — MiniMax-M3 90.9%, GLM-5 87.8%; OTIS Mock AIME 2024–2025 — GLM-5 80.0%, MiniMax-M3 71.1%.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M3 yet, so there is no like-for-like coding score. On overall capability, MiniMax-M3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
MiniMax-M3 has the largest context window at 1,048,576 tokens, against 204,800 for GLM-5. Maximum output per response: MiniMax-M3 up to 512,000, GLM-5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M3 accepts text, images and video; GLM-5 accepts text. MiniMax-M3 handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
MiniMax-M3 is the newest, released Jun 1, 2026. GLM-5 came out Feb 12, 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.
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