GLM-4.7-Flash vs MiMo-V2-Flash vs Gemma 3 27B IT
Too close to call on our weighted score (Gemma 3 27B IT 62, MiMo-V2-Flash 60, GLM-4.7-Flash 59). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7-Flash
59/100- ECI—
- Price$0.07 / $0.40
- Context200K
XiaomiMiMo-V2-Flash
60/100- ECI—
- Price$0.10 / $0.30
- Context262K
GoogleGemma 3 27B IT
62/100- ECI129.9
- Price$0.11 / $0.30
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Gemma 3 27B IT 62/100, MiMo-V2-Flash 60/100, GLM-4.7-Flash 59/100), so choose by what matters most for your work: MiMo-V2-Flash on price and MiMo-V2-Flash 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 priceMiMo-V2-FlashMiMo-V2-Flash $0.15 · GLM-4.7-Flash $0.152 · Gemma 3 27B IT $0.157 per 1M tokens (3:1 blend)
- Longest contextMiMo-V2-FlashMiMo-V2-Flash 262,144 · GLM-4.7-Flash 200,000 · Gemma 3 27B IT 131,072 tokens
- Widest inputsGemma 3 27B ITGLM-4.7-Flash: Text · MiMo-V2-Flash: Text · Gemma 3 27B IT: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7-Flash | MiMo-V2-Flash | Gemma 3 27B IT |
|---|---|---|---|---|
| Price | 50% | 85 | 85 | 84 |
| Inputs & features | 30% | 35 | 35 | 50 |
| Context window | 20% | 32 | 37 | 25 |
| Overall | 100% | 59/100 | 60/100 | 62/100 |
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 | GLM-4.7-FlashZ.ai (Zhipu) | MiMo-V2-FlashXiaomi | Gemma 3 27B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 129.9 |
| ECI rank | — | — | #137 of 165 |
| GPQA DiamondGraduate-level science questions | 45.1% | — | 47.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 25.0% (best) | — | 22.5% |
| Price per million tokens | |||
| Input | $0.07 (best) | $0.10 | $0.11 |
| Output | $0.40 | $0.30 | $0.30 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.152 | $0.15 (best) | $0.157 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 15 providers | Median of 3 providers | Median of 16 providers |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 131,072 tokens (best) | 65,536 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7-flash | — | — |
| API providers | 24 (best) | 4 | 17 |
| Released | Jan 19, 2026 | Dec 16, 2025 | Mar 12, 2025 |
| Knowledge cutoff | Apr 2025 | Dec 1, 2024 | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.7-Flash$1.50
- MiMo-V2-Flash$1.60
- Gemma 3 27B IT$1.69
Which should you choose?
Which is better: GLM-4.7-Flash, MiMo-V2-Flash or Gemma 3 27B IT?
It is close. Our weighted score puts them within 2 points (Gemma 3 27B IT 62/100, MiMo-V2-Flash 60/100, GLM-4.7-Flash 59/100), so choose by what matters most for your work: MiMo-V2-Flash on price and MiMo-V2-Flash 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, GLM-4.7-Flash, MiMo-V2-Flash or Gemma 3 27B IT?
MiMo-V2-Flash is cheaper at $0.10 input / $0.30 output per million tokens (median across 3 API providers). GLM-4.7-Flash costs $0.07 input / $0.40 output per million tokens (median across 15 API providers; free on Z.AI); Gemma 3 27B IT costs $0.11 input / $0.30 output per million tokens (median across 16 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for MiMo-V2-Flash versus $0.152 for GLM-4.7-Flash (1× as much) and $0.157 for Gemma 3 27B IT (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7-Flash has not been scored yet, MiMo-V2-Flash has not been scored yet and Gemma 3 27B IT has an ECI of 129.9.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7-Flash, MiMo-V2-Flash and Gemma 3 27B IT yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
MiMo-V2-Flash has the largest context window at 262,144 tokens, against 200,000 for GLM-4.7-Flash and 131,072 for Gemma 3 27B IT. Maximum output per response: GLM-4.7-Flash up to 131,072, MiMo-V2-Flash up to 65,536, Gemma 3 27B IT up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7-Flash accepts text; MiMo-V2-Flash accepts text; Gemma 3 27B IT accepts text and images. Gemma 3 27B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
GLM-4.7-Flash is the newest, released Jan 19, 2026. MiMo-V2-Flash came out Dec 16, 2025; Gemma 3 27B IT came out Mar 12, 2025. Knowledge cutoff: GLM-4.7-Flash Apr 2025, MiMo-V2-Flash Dec 1, 2024, Gemma 3 27B IT Aug 2024.
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.
