Step 3.5 Flash 2603 vs Rnj-1 Instruct vs GLM-4.7-FlashX
Step 3.5 Flash 2603 comes out ahead, 63 to 59 and 50 on our weighted score.
- Our pick
StepFunStep 3.5 Flash 2603
63/100- ECI—
- Price$0.10 / $0.30
- Context256K
Essential AI
Rnj-1 Instruct
50/100- ECI—
- Price$0.15 / $0.15
- Context33K
Z.ai (Zhipu)GLM-4.7-FlashX
59/100- ECI—
- Price$0.07 / $0.40
- Context200K
Step 3.5 Flash 2603 is our pick
Step 3.5 Flash 2603 is the better all-round choice, scoring 63/100 against GLM-4.7-FlashX (59) and Rnj-1 Instruct (50). 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 priceStep 3.5 Flash 2603 and Rnj-1 InstructStep 3.5 Flash 2603 $0.15 · Rnj-1 Instruct $0.15 · GLM-4.7-FlashX $0.152 per 1M tokens (3:1 blend)
- Longest contextStep 3.5 Flash 2603Step 3.5 Flash 2603 256,000 · GLM-4.7-FlashX 200,000 · Rnj-1 Instruct 32,768 tokens
- Widest inputsSame inputsStep 3.5 Flash 2603: Text · Rnj-1 Instruct: Text · GLM-4.7-FlashX: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Step 3.5 Flash 2603 | Rnj-1 Instruct | GLM-4.7-FlashX |
|---|---|---|---|---|
| Price | 50% | 85 | 85 | 85 |
| Inputs & features | 30% | 45 | 25 | 35 |
| Context window | 20% | 36 | 0 | 32 |
| Overall | 100% | 63/100 | 50/100 | 59/100 |
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 | Step 3.5 Flash 2603StepFun | Rnj-1 InstructEssential AI | GLM-4.7-FlashXZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.10 | $0.15 | $0.07 (best) |
| Output | $0.30 | $0.15 (best) | $0.40 |
| Cached input | $0.02 | — | $0.01 (best) |
| Blended (3:1) | $0.15 (best) | $0.15 (best) | $0.152 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official StepFun (Global) API | Median of 1 providers | Official Z.AI API |
| Limits | |||
| Context window | 256,000 tokens (best) | 32,768 tokens | 200,000 tokens |
| Max output | 256,000 tokens (best) | 32,768 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · high | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | Open |
| API model ID | step-3.5-flash-2603 | — | glm-4.7-flashx |
| API providers | 4 | 1 | 10 (best) |
| Released | Apr 2, 2026 | Dec 4, 2025 | Jan 19, 2026 |
| Knowledge cutoff | Jan 2025 | — | 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.
- Step 3.5 Flash 2603$1.60
- Rnj-1 Instruct$1.80
- GLM-4.7-FlashX$1.50
Which should you choose?
Which is better: Step 3.5 Flash 2603, Rnj-1 Instruct or GLM-4.7-FlashX?
Step 3.5 Flash 2603 is the better all-round choice, scoring 63/100 against GLM-4.7-FlashX (59) and Rnj-1 Instruct (50). 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, Step 3.5 Flash 2603, Rnj-1 Instruct or GLM-4.7-FlashX?
Step 3.5 Flash 2603 is cheaper at $0.10 input / $0.30 output per million tokens (official StepFun (Global) API price). Rnj-1 Instruct costs $0.15 input / $0.15 output per million tokens (median across 1 API provider); GLM-4.7-FlashX costs $0.07 input / $0.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Step 3.5 Flash 2603 versus $0.15 for Rnj-1 Instruct (1× as much) and $0.152 for GLM-4.7-FlashX (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Step 3.5 Flash 2603 has not been scored yet, Rnj-1 Instruct has not been scored yet and GLM-4.7-FlashX has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Step 3.5 Flash 2603, Rnj-1 Instruct and GLM-4.7-FlashX 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?
Step 3.5 Flash 2603 has the largest context window at 256,000 tokens, against 200,000 for GLM-4.7-FlashX and 32,768 for Rnj-1 Instruct. Maximum output per response: Step 3.5 Flash 2603 up to 256,000, Rnj-1 Instruct up to 32,768, GLM-4.7-FlashX up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Step 3.5 Flash 2603 accepts text; Rnj-1 Instruct accepts text; GLM-4.7-FlashX accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Step 3.5 Flash 2603 is the newest, released Apr 2, 2026. GLM-4.7-FlashX came out Jan 19, 2026; Rnj-1 Instruct came out Dec 4, 2025. Knowledge cutoff: Step 3.5 Flash 2603 Jan 2025, GLM-4.7-FlashX 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.
