GLM-4.5 vs Kimi K2 Instruct 0905 vs Morph V3 Large
Too close to call on our weighted score (GLM-4.5 39, Kimi K2 Instruct 0905 38, Morph V3 Large 30). The right pick depends on what you value most.
Z.ai (Zhipu)
GLM-4.5
39/100- ECI—
- Price$0.60 / $2.20
- Context131K
Moonshot AI
Kimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
Morph
Morph V3 Large
30/100- ECI—
- Price$0.90 / $1.90
- Context262K
Too close to call
It is close. Our weighted score puts them within 1 points (GLM-4.5 39/100, Kimi K2 Instruct 0905 38/100, Morph V3 Large 30/100), so choose by what matters most for your work: GLM-4.5 on price. 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 priceGLM-4.5GLM-4.5 $1.00 · Kimi K2 Instruct 0905 $1.07 · Morph V3 Large $1.15 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905 and Morph V3 LargeKimi K2 Instruct 0905 262,144 · Morph V3 Large 262,144 · GLM-4.5 131,072 tokens
- Widest inputsSame inputsGLM-4.5: Text · Kimi K2 Instruct 0905: Text · Morph V3 Large: Text
- Self-hostingGLM-4.5 and Kimi K2 Instruct 0905Publishes downloadable weights (Modified MIT)
| Measure | Weight | GLM-4.5 | Kimi K2 Instruct 0905 | Morph V3 Large |
|---|---|---|---|---|
| Price | 50% | 48 | 46 | 45 |
| Inputs & features | 30% | 35 | 25 | 0 |
| Context window | 20% | 25 | 37 | 37 |
| Overall | 100% | 39/100 | 38/100 | 30/100 |
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 | Morph V3 LargeMorph | ||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 (best) | $0.60 (best) | $0.90 |
| Output | $2.20 | $2.50 | $1.90 (best) |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 (best) | $1.07 | $1.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 12 providers | Median of 5 providers |
| Limits | |||
| Context window | 131,072 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 98,304 tokens | 98,304 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenModified MIT | Proprietary |
| API model ID | glm-4.5 | — | — |
| API providers | 19 (best) | 13 | 5 |
| Released | Jul 28, 2025 | Sep 5, 2025 | Jul 7, 2025 |
| Knowledge cutoff | Apr 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-4.5$10.40
Kimi K2 Instruct 0905$11.00
- Morph V3 Large$12.80
Which should you choose?
Which is better: GLM-4.5, Kimi K2 Instruct 0905 or Morph V3 Large?
It is close. Our weighted score puts them within 1 points (GLM-4.5 39/100, Kimi K2 Instruct 0905 38/100, Morph V3 Large 30/100), so choose by what matters most for your work: GLM-4.5 on price. 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, GLM-4.5, Kimi K2 Instruct 0905 or Morph V3 Large?
GLM-4.5 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Kimi K2 Instruct 0905 costs $0.60 input / $2.50 output per million tokens (median across 12 API providers); Morph V3 Large costs $0.90 input / $1.90 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.5 versus $1.07 for Kimi K2 Instruct 0905 (1.1× as much) and $1.15 for Morph V3 Large (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5 has not been scored yet, Kimi K2 Instruct 0905 has not been scored yet and Morph V3 Large has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5, Kimi K2 Instruct 0905 and Morph V3 Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Morph V3 Large does not support tool calling, which most coding agents need.
Which has the bigger context window?
Kimi K2 Instruct 0905 and Morph V3 Large have the largest context windows (262,144 and 262,144 tokens), against 131,072 for GLM-4.5. Maximum output per response: GLM-4.5 up to 98,304, Kimi K2 Instruct 0905 up to 98,304, Morph V3 Large up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5 accepts text; Kimi K2 Instruct 0905 accepts text; Morph V3 Large accepts text. They handle the same number of input types.
Are any of these open source?
GLM-4.5 and Kimi K2 Instruct 0905 publishes its weights (Modified MIT) and can be self-hosted; Morph V3 Large is proprietary.
Which is newer?
Kimi K2 Instruct 0905 is the newest, released Sep 5, 2025. GLM-4.5 came out Jul 28, 2025; Morph V3 Large came out Jul 7, 2025. Knowledge cutoff: GLM-4.5 Apr 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 Nov 2, 2026.