Grok 4 Fast Reasoning vs Sarvam 105B vs GLM-4.6V-Flash
Grok 4 Fast Reasoning comes out ahead, 75 to 63 and 52 on our weighted score, though GLM-4.6V-Flash is 5% cheaper per token.
- Our pick
xAIGrok 4 Fast Reasoning
75/100- ECI—
- Price$0.20 / $0.50
- Context2M
Sarvam AI
Sarvam 105B
52/100- ECI—
- Price$0.158 / $0.632
- Context131K
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
Grok 4 Fast Reasoning is our pick
Grok 4 Fast Reasoning is the better all-round choice, scoring 75/100 against GLM-4.6V-Flash (63) and Sarvam 105B (52). It leads on inputs & features and context window. 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-4.6V-FlashGLM-4.6V-Flash $0.261 · Grok 4 Fast Reasoning $0.275 · Sarvam 105B $0.276 per 1M tokens (3:1 blend)
- Longest contextGrok 4 Fast ReasoningGrok 4 Fast Reasoning 2,000,000 · Sarvam 105B 131,072 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsGrok 4 Fast ReasoningGrok 4 Fast Reasoning: Text, Images, Audio, Video · Sarvam 105B: Text · GLM-4.6V-Flash: Text, Images, Video
- Self-hostingSarvam 105B and GLM-4.6V-FlashPublishes downloadable weights
| Measure | Weight | Grok 4 Fast Reasoning | Sarvam 105B | GLM-4.6V-Flash |
|---|---|---|---|---|
| Price | 50% | 73 | 73 | 74 |
| Inputs & features | 30% | 80 | 35 | 70 |
| Context window | 20% | 72 | 25 | 24 |
| Overall | 100% | 75/100 | 52/100 | 63/100 |
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 | Grok 4 Fast ReasoningxAI | Sarvam 105BSarvam AI | GLM-4.6V-FlashZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.20 | $0.158 (best) | $0.161 |
| Output | $0.50 (best) | $0.632 | $0.559 |
| Cached input | — | $0.053 | — |
| Blended (3:1) | $0.275 | $0.276 | $0.261 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Official Sarvam AI API | Median of 2 providers |
| Limits | |||
| Context window | 2,000,000 tokens (best) | 131,072 tokens | 128,000 tokens |
| Max output | 2,000,000 tokens (best) | 131,072 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | sarvam-105b | glm-4.6v-flash |
| API providers | 5 | 3 | 6 (best) |
| Released | Dec 18, 2025 | Feb 18, 2026 | Dec 8, 2025 |
| 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.
- Grok 4 Fast Reasoning$3.00
- Sarvam 105B$2.84
- GLM-4.6V-Flash$2.73
Which should you choose?
Which is better: Grok 4 Fast Reasoning, Sarvam 105B or GLM-4.6V-Flash?
Grok 4 Fast Reasoning is the better all-round choice, scoring 75/100 against GLM-4.6V-Flash (63) and Sarvam 105B (52). It leads on inputs & features and context window. 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, Grok 4 Fast Reasoning, Sarvam 105B or GLM-4.6V-Flash?
GLM-4.6V-Flash is cheaper at $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI). Grok 4 Fast Reasoning costs $0.20 input / $0.50 output per million tokens (median across 4 API providers); Sarvam 105B costs $0.158 input / $0.632 output per million tokens (official Sarvam AI API price). At a typical mix of three input tokens to one output token, that is $0.261 per million tokens for GLM-4.6V-Flash versus $0.275 for Grok 4 Fast Reasoning (1.1× as much) and $0.276 for Sarvam 105B (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Grok 4 Fast Reasoning has not been scored yet, Sarvam 105B has not been scored yet and GLM-4.6V-Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Grok 4 Fast Reasoning, Sarvam 105B and GLM-4.6V-Flash 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?
Grok 4 Fast Reasoning has the largest context window at 2,000,000 tokens, against 131,072 for Sarvam 105B and 128,000 for GLM-4.6V-Flash. Maximum output per response: Grok 4 Fast Reasoning up to 2,000,000, Sarvam 105B up to 131,072, GLM-4.6V-Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Grok 4 Fast Reasoning accepts text, images, audio and video; Sarvam 105B accepts text; GLM-4.6V-Flash accepts text, images and video. Grok 4 Fast Reasoning handles the widest range of inputs.
Are any of these open source?
Sarvam 105B and GLM-4.6V-Flash publishes its weights and can be self-hosted; Grok 4 Fast Reasoning is proprietary.
Which is newer?
Sarvam 105B is the newest, released Feb 18, 2026. Grok 4 Fast Reasoning came out Dec 18, 2025; GLM-4.6V-Flash came out Dec 8, 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.
