Relace Apply 3 vs Kimi K2 Instruct 0905 vs GLM-4.6
Too close to call on our weighted score (GLM-4.6 41, Kimi K2 Instruct 0905 38, Relace Apply 3 32). The right pick depends on what you value most.
Relace
Relace Apply 3
32/100- ECI—
- Price$0.85 / $1.25
- Context256K
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
Z.ai (Zhipu)GLM-4.6
41/100- ECI—
- Price$0.60 / $2.20
- Context205K
Too close to call
It is close. Our weighted score puts them within 3 points (GLM-4.6 41/100, Kimi K2 Instruct 0905 38/100, Relace Apply 3 32/100), so choose by what matters most for your work: Relace Apply 3 on price and Kimi K2 Instruct 0905 for long inputs. 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 priceRelace Apply 3Relace Apply 3 $0.95 · GLM-4.6 $1.00 · Kimi K2 Instruct 0905 $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · Relace Apply 3 256,000 · GLM-4.6 204,800 tokens
- Widest inputsSame inputsRelace Apply 3: Text · Kimi K2 Instruct 0905: Text · GLM-4.6: Text
- Self-hostingKimi K2 Instruct 0905 and GLM-4.6Publishes downloadable weights (Modified MIT)
| Measure | Weight | Relace Apply 3 | Kimi K2 Instruct 0905 | GLM-4.6 |
|---|---|---|---|---|
| Price | 50% | 49 | 46 | 48 |
| Inputs & features | 30% | 0 | 25 | 35 |
| Context window | 20% | 36 | 37 | 32 |
| Overall | 100% | 32/100 | 38/100 | 41/100 |
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 | Relace Apply 3Relace | Kimi K2 Instruct 0905Moonshot AI | GLM-4.6Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.85 | $0.60 (best) | $0.60 (best) |
| Output | $1.25 (best) | $2.50 | $2.20 |
| Cached input | — | — | $0.11 |
| Blended (3:1) | $0.95 (best) | $1.07 | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 12 providers | Official Z.AI API |
| Limits | |||
| Context window | 256,000 tokens | 262,144 tokens (best) | 204,800 tokens |
| Max output | 128,000 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 | No | No | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenModified MIT | Open |
| API model ID | — | — | glm-4.6 |
| API providers | 3 | 13 | 29 (best) |
| Released | Sep 26, 2025 | Sep 5, 2025 | Sep 30, 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.
- Relace Apply 3$11.00
- Kimi K2 Instruct 0905$11.00
- GLM-4.6$10.40
Which should you choose?
Which is better: Relace Apply 3, Kimi K2 Instruct 0905 or GLM-4.6?
It is close. Our weighted score puts them within 3 points (GLM-4.6 41/100, Kimi K2 Instruct 0905 38/100, Relace Apply 3 32/100), so choose by what matters most for your work: Relace Apply 3 on price and Kimi K2 Instruct 0905 for long inputs. 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, Relace Apply 3, Kimi K2 Instruct 0905 or GLM-4.6?
Relace Apply 3 is cheaper at $0.85 input / $1.25 output per million tokens (median across 3 API providers). GLM-4.6 costs $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). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Relace Apply 3 versus $1.00 for GLM-4.6 (1.1× as much) and $1.07 for Kimi K2 Instruct 0905 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Relace Apply 3 has not been scored yet, Kimi K2 Instruct 0905 has not been scored yet and GLM-4.6 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Relace Apply 3, Kimi K2 Instruct 0905 and GLM-4.6 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Relace Apply 3 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Kimi K2 Instruct 0905 has the largest context window at 262,144 tokens, against 256,000 for Relace Apply 3 and 204,800 for GLM-4.6. Maximum output per response: Relace Apply 3 up to 128,000, Kimi K2 Instruct 0905 up to 98,304, GLM-4.6 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Relace Apply 3 accepts text; Kimi K2 Instruct 0905 accepts text; GLM-4.6 accepts text. They handle the same number of input types.
Are any of these open source?
Kimi K2 Instruct 0905 and GLM-4.6 publishes its weights (Modified MIT) and can be self-hosted; Relace Apply 3 is proprietary.
Which is newer?
GLM-4.6 is the newest, released Sep 30, 2025. Relace Apply 3 came out Sep 26, 2025; Kimi K2 Instruct 0905 came out Sep 5, 2025. Knowledge cutoff: GLM-4.6 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.
